# tfest

Estimate transfer function model

## Syntax

## Description

### Estimate Transfer Function Model

estimates the continuous-time transfer function `sys`

= tfest(`tt`

,`np`

)`sys`

with
`np`

poles, using all the input and output signals in the timetable
`tt`

. The number of zeros in sys is max(`np`

-1,0).
You can use this syntax for SISO and MISO systems. The function assumes that the last
variable in the timetable is the single output signal.

You cannot use `tfest`

to estimate time-series models, which are
models that contain no inputs. Use `ar`

, `arx`

, or `armax`

for time-series models instead.

estimates a continuous-time transfer function using the time-domain input signals and
output signals in the matrices `sys`

= tfest(`u`

,`y`

,`np`

)`u`

,`y`

. The software
assumes that the data sample time is 1 second. You cannot change this assumed sample time.
If you want to estimate a model from data with a sample time other than 1 second, you have
two alternatives:

Estimate a discrete-time system instead by setting the sample time using the

`'Ts'`

name-value argument. For example,`sys = tfest(u,y,np,'Ts',0.1)`

sets the sample time to`0.1`

. You can use this use syntax with SISO, MISO, and MIMO systems.Convert your matrix data to a

`timetable`

or`iddata`

object prior to estimating a continuous-time system. These formats allow you to incorporate sample-time knowledge into the data. For more information, see`u`

,`y`

.

Estimating continuous-time models from matrix-based data is not recommended.

uses the time-domain or frequency-domain data in `sys`

= tfest(`data`

,`np`

)`data`

. Use this
syntax especially when you want to estimate a transfer function using frequency-domain or
frequency response data, or when you want to take advantage of the additional information,
such as intersample behavior, data sample time, or experiment labeling, that data objects
provide.

uses additional model options specified by one or more name-value pair arguments. For
example, specify a discrete-time system from matrix data that has a sample time of 0.1
using `sys`

= tfest(___,`Name,Value`

)`sys = tfest(um,ym,np,'Ts',0.1)`

. Specify input and output signal
variable names that correspond with the variables to use for MIMO timetable data using
```
sys =
tfest(data,np,nz,'InputNames',["u1","u2"],'OutputNames',["y1","y3"])
```

.

### Configure Initial Parameters

### Specify Additional Estimation Options

### Return Estimated Initial Conditions

`[`

returns the estimated initial conditions as an `sys`

,`ic`

] = tfest(___)`initialCondition`

object. Use this syntax if you plan to simulate or predict the model response using the
same estimation input data and then compare the response with the same estimation output
data. Incorporating the initial conditions yields a better match between measured and
simulated or predicted data during the early stage of the simulation.

## Examples

### Estimate Transfer Function Model by Specifying Number of Poles

Load the time-domain system-response data in timetable `tt1`

.

load sdata1.mat tt1;

Set the number of poles `np`

to `2`

and estimate a transfer function.

np = 2; sys = tfest(tt1,np);

`sys`

is an `idtf`

model containing the estimated two-pole transfer function.

View the numerator and denominator coefficients of the resulting estimated model `sys`

.

sys.Numerator

`ans = `*1×2*
2.4554 176.9856

sys.Denominator

`ans = `*1×3*
1.0000 3.1625 23.1631

To view the uncertainty in the estimates of the numerator and denominator and other information, use `tfdata`

.

### Specify Number of Poles and Zeros in Estimated Transfer Function

Load time-domain system response data `z2`

and use it to estimate a transfer function that contains two poles and one zero.

load iddata2 z2; np = 2; nz = 1; sys = tfest(z2,np,nz);

`sys`

is an `idtf`

model containing the estimated transfer function.

### Estimate Transfer Function Containing Known Transport Delay

Load the data `z2`

, which is an `iddata`

object that contains time-domain system response data.

load iddata2 z2;

Estimate a transfer function model `sys`

that contains two poles and one zero, and which includes a known transport delay `iodelay`

.

np = 2; nz = 1; iodelay = 0.2; sys = tfest(z2,np,nz,iodelay);

`sys`

is an `idtf`

model containing the estimated transfer function, with the `IODelay`

property set to 0.2 seconds.

### Estimate Transfer Function Containing Unknown Transport Delay

Load time-domain system response data `z2`

and use it to estimate a two-pole one-zero transfer function for the system. Specify an unknown transport delay for the transfer function by setting the value of `iodelay`

to `NaN`

.

load iddata2 z2; np = 2; nz = 1; iodelay = NaN; sys = tfest(z2,np,nz,iodelay);

`sys`

is an `idtf`

model containing the estimated transfer function, whose `IODelay`

property is estimated using the data.

### Estimate Discrete-Time Transfer Function

Load time-domain system response data, which is contained in input and output matrices `umat2`

and `ymat2`

.

load sdata2.mat umat2 ymat2

Estimate a discrete-time transfer function with two poles and one zero. Specify the sample time `Ts`

as 0.1 seconds and the transport delay `iodelay`

as 2 seconds.

```
np = 2;
nz = 1;
iodelay = 2;
Ts = 0.1;
sysd = tfest(umat2,ymat2,np,nz,iodelay,'Ts',Ts)
```

sysd = From input "u1" to output "y1": 1.8 z^-1 z^(-2) * ---------------------------- 1 - 1.418 z^-1 + 0.6613 z^-2 Sample time: 0.1 seconds Discrete-time identified transfer function. Parameterization: Number of poles: 2 Number of zeros: 1 Number of free coefficients: 3 Use "tfdata", "getpvec", "getcov" for parameters and their uncertainties. Status: Estimated using TFEST on time domain data. Fit to estimation data: 80.26% FPE: 2.095, MSE: 2.063

By default, the model has no feedthrough, and the numerator polynomial of the estimated transfer function has a zero leading coefficient `b0`

. To estimate `b0`

, specify the `Feedthrough`

property during estimation.

### Estimate Discrete-Time Transfer Function with Feedthrough

Load the estimation data `z5`

.

load iddata5 z5

First, estimate a discrete-time transfer function model with two poles, one zero, and no feedthrough. Get the sample time from the `Ts`

property of `z5`

.

```
np = 2;
nz = 1;
sys = tfest(z5,np,nz,'Ts',z5.Ts);
```

The estimated transfer function has the following form:

$$H({z}^{-1})=\frac{b1{z}^{-1}+b2{z}^{-2}}{1+a1{z}^{-1}+a2{z}^{-2}}$$

By default, the model has no feedthrough, and the numerator polynomial of the estimated transfer function has a zero leading coefficient `b0`

. To estimate `b0`

, specify the `Feedthrough`

property during estimation.

sys = tfest(z5,np,nz,'Ts',z5.Ts,'Feedthrough',true);

The numerator polynomial of the estimated transfer function now has a nonzero leading coefficient:

$$H({z}^{-1})=\frac{b0+b1{z}^{-1}+b2{z}^{-2}}{1+a1{z}^{-1}+a2{z}^{-2}}$$

### Analyze Origin of Delay in Measured Data

Compare two discrete-time models with and without feedthrough and transport delay.

If there is a delay from the measured input to output, it can be attributed either to a lack of feedthrough or to an actual transport delay. For discrete-time models, absence of feedthrough corresponds to a lag of one sample between the input and output. Estimating a model using `Feedthrough = false`

and `iodelay = 0`

thus produces a discrete-time system that is equivalent to a system estimated using `Feedthrough = true`

and `iodelay = 1`

. Both systems show the same time- and frequency-domain responses, for example, on step and Bode plots. However, you get different results if you reduce these models using `balred`

or convert them to their continuous-time representations. Therefore, a best practice is to check if the observed delay can be attributed to a transport delay or to a lack of feedthrough.

Estimate a discrete-time model with no feedthrough.

load iddata1 z1 np = 2; nz = 2; sys1 = tfest(z1,np,nz,'Ts',z1.Ts);

Because `sys1`

has no feedthrough and therefore has a numerator polynomial that beings with $${z}^{-1}$$, `sys1`

has a lag of one sample. The `IODelay`

property is 0.

Estimate another discrete-time model with feedthrough and with a reduction from two zeros to one, incurring a one-sample input-output delay.

sys2 = tfest(z1,np,nz-1,1,'Ts',z1.Ts,'Feedthrough',true);

Compare the Bode responses of the models.

bode(sys1,sys2);

The discrete equations that underlie sys`1`

and sys`2`

are equivalent, and so are the Bode responses.

Convert the models to continuous time and compare the Bode responses for these models.

sys1c = d2c(sys1); sys2c = d2c(sys2); bode(sys1c,sys2c); legend

As the plot shows, the Bode responses of the two models do not match when you convert them to continuous time. When there is no feedthrough, as with `sys1c`

, there must be some lag. When there is feedthrough, as with `sys2c`

, there can be no lag. Continuous-time feedthrough maps to discrete-time feedthrough. Continuous-time lag maps to discrete-time delays.

### Estimate MISO Discrete-Time Transfer Function with Feedthrough and Delay Specifications for Individual Channels

Estimate a two-input, one-output discrete-time transfer function with a delay of two 2 samples on the first input and zero samples on the second input. Both inputs have no feedthrough.

Load the data and split the data into estimation and validation data sets.

load iddata7 z7 ze = z7(1:300); zv = z7(200:400);

Estimate a two-input, one-output transfer function with two poles and one zero for each input-to-output transfer function.

Lag = [2;0]; Ft = [false,false]; model = tfest(ze,2,1,'Ts',z7.Ts,'Feedthrough',Ft,'InputDelay',Lag);

The `Feedthrough`

value you choose dictates whether the leading numerator coefficient is zero (no feedthrough) or not (nonzero feedthrough). Delays are generally expressed separately using the `InputDelay`

or `IODelay`

property. This example uses `InputDelay`

only to express the delays.

Validate the estimated model. Exclude the data outliers for validation.

```
I = 1:201;
I(114:118) = [];
opt = compareOptions('Samples',I);
compare(zv,model,opt)
```

### Estimate Transfer Function Model Using Regularized Impulse Response Model

Identify a 15th order transfer function model by using regularized impulse response estimation.

Load the data.

load regularizationExampleData m0simdata;

Obtain a regularized impulse response (FIR) model.

opt = impulseestOptions('RegularizationKernel','DC'); m0 = impulseest(m0simdata,70,opt);

Convert the model into a transfer function model after reducing the order to 15.

m = idtf(balred(idss(m0),15));

Compare the model output with the data.

compare(m0simdata,m);

### Estimate Transfer Function Using Estimation Option Set

Create an option set for `tfest`

that specifies the initialization and search methods. Also set the display option, which specifies that the loss-function values for each iteration be shown.

opt = tfestOptions('InitializeMethod','n4sid','Display','on','SearchMethod','lsqnonlin');

Load time-domain system response data `z2`

and use it to estimate a transfer function with two poles and one zero. Specify `opt`

for the estimation options.

load iddata2 z2; np = 2; nz = 1; iodelay = 0.2; sys = tfest(z2,np,nz,iodelay,opt);

`sys`

is an `idtf`

model containing the estimated transfer function.

### Specify Model Properties of Estimated Transfer Function

Load the time-domain system response data `z2`

, and use it to estimate a two-pole, one-zero transfer function. Specify an input delay.

load iddata2 z2; np = 2; nz = 1; input_delay = 0.2; sys = tfest(z2,np,nz,'InputDelay',input_delay);

`sys`

is an `idtf`

model containing the estimated transfer function with an input delay of 0.2 seconds.

### Convert Frequency-Response Data into Transfer Function

Use `bode`

to obtain the magnitude and phase response for the following system:

$$H(s)=\frac{s+0.2}{{s}^{3}+2{s}^{2}+s+1}$$

Use 100 frequency points, ranging from 0.1 rad/s to 10 rad/s, to obtain the frequency-response data. Use `frd`

to create a frequency-response data object.

freq = logspace(-1,1,100); [mag,phase] = bode(tf([1 0.2],[1 2 1 1]),freq); data = frd(mag.*exp(1j*phase*pi/180),freq);

Estimate a three-pole, one-zero transfer function using `data`

.

np = 3; nz = 1; sys = tfest(data,np,nz);

`sys`

is an `idtf`

model containing the estimated transfer function.

### Estimate Transfer Function with Known Transport Delays for Multiple Inputs

Load the time-domain system response data `co2data`

, which contains the data from two experiments, each with two inputs and one output. Convert the data from the first experiment into an `iddata`

object `data`

with a sample time of 0.5 seconds.

```
load co2data;
Ts = 0.5;
data = iddata(Output_exp1,Input_exp1,Ts);
```

Specify estimation options for the search method and the input and output offsets. Also specify the maximum number of search iterations.

opt = tfestOptions('SearchMethod','gna'); opt.InputOffset = [170;50]; opt.OutputOffset = mean(data.y(1:75)); opt.SearchOptions.MaxIterations = 50;

Estimate a transfer function using the measured data and the estimation option set `opt`

. Specify the transport delays from the inputs to the output.

np = 3; nz = 1; iodelay = [2 5]; sys = tfest(data,np,nz,iodelay,opt);

`iodelay`

specifies the input-to-output delay from the first and second inputs to the output as 2 seconds and 5 seconds, respectively.

`sys`

is an `idtf`

model containing the estimated transfer function.

### Estimate Transfer Function with Known and Unknown Transport Delays

Load time-domain system response data and use it to estimate a transfer function for the system. Specify the known and unknown transport delays.

```
load co2data;
Ts = 0.5;
data = iddata(Output_exp1,Input_exp1,Ts);
```

`data`

is an `iddata`

object with two input channels and one output channels, and which has a sample rate of 0.5 seconds.

Create an option set `opt`

. Specify estimation options for the search method and the input and output offsets. Also specify the maximum number of search iterations.

opt = tfestOptions('Display','on','SearchMethod','gna'); opt.InputOffset = [170; 50]; opt.OutputOffset = mean(data.y(1:75)); opt.SearchOptions.MaxIterations = 50;

Specify the unknown and known transport delays in `iodelay`

, using `2`

for a known delay of 2 seconds and `nan`

for the unknown delay. Estimate the transfer function using `iodelay`

and `opt`

.

np = 3; nz = 1; iodelay = [2 nan]; sys = tfest(data,np,nz,iodelay,opt);

`sys`

is an `idtf`

model containing the estimated transfer function.

### Estimate Transfer Function with Unknown, Constrained Transport Delays

Create a transfer function model with the expected numerator and denominator structure and delay constraints.

In this example, the experiment data consists of two inputs and one output. Both transport delays are unknown and have an identical upper bound. Additionally, the transfer functions from both inputs to the output are identical in structure.

```
init_sys = idtf(NaN(1,2),[1,NaN(1,3)],'IODelay',NaN);
init_sys.Structure(1).IODelay.Free = true;
init_sys.Structure(1).IODelay.Maximum = 7;
```

`init_sys`

is an `idtf`

model describing the structure of the transfer function from one input to the output. The transfer function consists of one zero, three poles, and a transport delay. The use of `NaN`

indicates unknown coefficients.

`init_sys.Structure(1).IODelay.Free = true`

indicates that the transport delay is not fixed.

`init_sys.Structure(1).IODelay.Maximum = 7`

sets the upper bound for the transport delay to 7 seconds.

Specify the transfer function from both inputs to the output.

init_sys = [init_sys,init_sys];

Load time-domain system response data and use it to estimate a transfer function. Specify options in the `tfestOptions`

option set `opt`

.

load co2data; Ts = 0.5; data = iddata(Output_exp1,Input_exp1,Ts); opt = tfestOptions('Display','on','SearchMethod','gna'); opt.InputOffset = [170;50]; opt.OutputOffset = mean(data.y(1:75)); opt.SearchOptions.MaxIterations = 50; sys = tfest(data,init_sys,opt);

`sys`

is an `idtf`

model containing the estimated transfer function.

Analyze the estimation result by comparison. Create a `compareOptions`

option set `opt2`

and specify input and output offsets, and then use `compare`

.

opt2 = compareOptions; opt2.InputOffset = opt.InputOffset; opt2.OutputOffset = opt.OutputOffset; compare(data,sys,opt2)

### Estimate Transfer Function Containing Different Numbers of Poles for Input-Output Pairs

Estimate a multiple-input, single-output transfer function containing different numbers of poles for input-output pairs for given data.

Obtain frequency-response data.

For example, use `frd`

to create a frequency-response data model for the following system:

$$G=\left[\begin{array}{c}{e}^{-4s}\frac{s+2}{{s}^{3}+2{s}^{2}+4s+5}\\ {e}^{-0.6s}\frac{5}{{s}^{4}+2{s}^{3}+{s}^{2}+s}\end{array}\right]$$

Use 100 frequency points, ranging from 0.01 rad/s to 100 rad/s, to obtain the frequency-response data.

```
G = tf({[1 2],[5]},{[1 2 4 5],[1 2 1 1 0]},0,'IODelay',[4 0.6]);
data = frd(G,logspace(-2,2,100));
```

`data`

is an `frd`

object containing the continuous-time frequency response for `G`

.

Estimate a transfer function for `data`

.

np = [3 4]; nz = [1 0]; iodelay = [4 0.6]; sys = tfest(data,np,nz,iodelay);

`np`

specifies the number of poles in the estimated transfer function. The first element of `np`

indicates that the transfer function from the first input to the output contains three poles. Similarly, the second element of `np`

indicates that the transfer function from the second input to the output contains four poles.

`nz`

specifies the number of zeros in the estimated transfer function. The first element of `nz`

indicates that the transfer function from the first input to the output contains one zero. Similarly, the second element of `np`

indicates that the transfer function from the second input to the output does not contain any zeros.

`iodelay`

specifies the transport delay from the first input to the output as 4 seconds. The transport delay from the second input to the output is specified as 0.6 seconds.

`sys`

is an `idtf`

model containing the estimated transfer function.

### Estimate Transfer Function for Unstable System

Estimate a transfer function describing an unstable system using frequency-response data.

Use `idtf`

to construct a transfer function model `G`

of the following system:

$$G=\left[\begin{array}{c}\frac{s+2}{{s}^{3}+2{s}^{2}+4s+5}\\ \frac{5}{{s}^{4}+2{s}^{3}+{s}^{2}+s+1}\end{array}\right]$$

G = idtf({[1 2], 5},{[1 2 4 5],[1 2 1 1 1]});

Use `idfrd`

to obtain a frequency-response data model `data`

for `G`

. Specify 100 frequency points ranging from 0.01 rad/s to 100 rad/s.

data = idfrd(G,logspace(-2,2,100));

`data`

is an `idfrd`

object.

Estimate a transfer function for `data`

.

np = [3 4]; nz = [1 0]; sys = tfest(data,np,nz);

`np`

specifies the number of poles in the estimated transfer function. The first element of `np`

indicates that the transfer function from the first input to the output contains three poles. Similarly, the second element of `np`

indicates that the transfer function from the second input to the output contains four poles.

`nz`

specifies the number of zeros in the estimated transfer function. The first element of `nz`

indicates that the transfer function from the first input to the output contains one zero. Similarly, the second element of `nz`

indicates that the transfer function from the second input to the output does not contain any zeros.

`sys`

is an `idtf`

model containing the estimated transfer function.

pole(sys)

`ans = `*7×1 complex*
-1.5260 + 0.0000i
-0.2370 + 1.7946i
-0.2370 - 1.7946i
-1.4656 + 0.0000i
-1.0000 + 0.0000i
0.2328 + 0.7926i
0.2328 - 0.7926i

`sys`

is an unstable system, as the pole display indicates.

### Estimate Transfer Function using High Modal Density Frequency Response Data

Load the high-density frequency-response measurement data. The data corresponds to an unstable process maintained at equilibrium using feedback control.

load HighModalDensityData FRF f

Package the data as an `idfrd`

object for identification and find the Bode magnitude response.

G = idfrd(permute(FRF,[2 3 1]),f,0,'FrequencyUnit','Hz'); bodemag(G)

Estimate a transfer function with 32 poles and 32 zeros, and compare the Bode magnitude response.

sys = tfest(G,32,32); bodemag(G, sys) xlim([0.01,2e3]) legend

### Obtain and Apply Estimated Initial Conditions

Load and plot the data.

load iddata1ic z1i plot(z1i)

Examine the initial value of the output data `y(1)`

.

ystart = z1i.y(1)

ystart = -3.0491

The measured output does not start at 0.

Estimate a second-order transfer function `sys`

and return the estimated initial condition `ic`

.

[sys,ic] = tfest(z1i,2,1); ic

ic = initialCondition with properties: A: [2x2 double] X0: [2x1 double] C: [0.2957 5.2441] Ts: 0

`ic`

is an `initialCondition`

object that encapsulates the free response of `sys`

, in state-space form, to the initial state vector in `X0`

.

Simulate `sys`

using the estimation data but without incorporating the initial conditions. Plot the simulated output with the measured output.

y_no_ic = sim(sys,z1i); plot(y_no_ic,z1i) legend('Model Response','Output Data')

The measured and simulated outputs do not agree at the beginning of the simulation.

Incorporate the initial condition into the `simOptions`

option set.

opt = simOptions('InitialCondition',ic); y_ic = sim(sys,z1i,opt); plot(y_ic,z1i) legend('Model Response','Output Data')

The simulation combines the model response to the input signal with the free response to the initial condition. The measured and simulated outputs now have better agreement at the beginning of the simulation. This initial condition is valid only for the estimation data `z1i`

.

## Input Arguments

`tt`

— Timetable-based estimation data

`timetable`

| cell array of `timetables`

Estimation data, specified as a uniformly sampled `timetable`

that contains both input and output signal variables
or, for multiexperiment data, a cell array of
timetables.

#### Use Entire Timetable

If you want to use all the input and output variables in `tt`

, and the
variables are organized so that the set of input
variables is followed by the set of output
variables, then:

For SISO systems, specify

`tt`

as an*N*-by-2_{s}`timetable`

, where*N*is the number of samples and the two_{s}`timetable`

variables represent the measured input signal and output signal respectively.For MIMO systems, specify

`tt`

as an*N*-by-(_{s}*N*+_{u}*N*)_{y}`timetable`

, where*N*is the number of inputs and_{u}*N*is the number of outputs. The first_{y}*N*variables must contain the input signals and the remaining_{u}*N*variables must contain the output signals._{y}When you are estimating state space or transfer function models, you must also explicitly specify the input and output channels, as the following section describes.

For multiexperiment data, specify data as an

*N*-by-1 cell array of timetables, where_{e}*N*is the number of experiments. The sample times of all the experiments must match._{e}

#### Use Selected Variables from Timetable

If you want to use a subset of variables from the `timetable`

, or if the input
and output variables are intermixed, use the
`'InputName'`

and
`'OutputName'`

name-value
arguments to specify which variables to
use.

For example, suppose that `tt`

contains six variables:
`"u1"`

, `"u2"`

, `"u3"`

, and
`"y1"`

, `"y2"`

, `"y3"`

. For estimation,
you want to use the variables `"u1"`

and `"u2"`

as the
inputs and the variables `"y1"`

and `"y3"`

as the outputs.
Use the following command to perform the estimation:

```
sys = tfest(tt,__,'InputName',["u1" "u2"],'OutputName',["y1"
"y3"])
```

For more information about working with estimation data types, see Data Types in System Identification Toolbox.

`u`

, `y`

— Matrix-based estimation data

matrices | cell array of matrices

Estimation data, specified for SISO systems as a pair of
*N _{s}*-by-1 real-valued matrices that
contain uniformly sampled input and output time-domain signal values. Here,

*N*is the number of samples.

_{s}For MIMO systems, specify `u`

,`y`

as an
input/output matrix pair with the following dimensions:

`u`

—*N*-by-_{s}*N*, where_{u}*N*is the number of inputs._{u}`y`

—*N*-by-_{s}*N*, where_{y}*N*is the number of outputs._{y}

For multiexperiment data, specify `u`

,`y`

as a
pair of 1-by-*N _{e}* cell arrays, where

*N*is the number of experiments. The sample times of all the experiments must match.

_{e}#### Limitations

Matrix-based data does not support estimation from frequency-domain data. You must use a data object such as an

`iddata`

object or`idfrd`

object (see`data`

).Using matrices for estimation data is not recommended for continuous-time estimation since the data does not provide the sample time. The software assumes that the data is sampled at 1 Hz. For continuous-time estimation, it is recommended that you convert the input and output matrix pair into a single

`timetable`

. For example, to convert the single-column matrices`um`

and`ym`

to a`timetable`

`tt`

with a sample time of 0.5 minutes, use the following command.For a more detailed example of converting matrix-based SISO data to a timetable, see Convert SISO Matrix Data to Timetable. For an example of converting a MIMO matrix pair to a timetable, see Convert MIMO Matrix Data to Timetable for Continuous-Time Model Estimation.`tt = timetable(um,ym,'rowtimes',minutes(0.5*(1:size(u,1))))`

For more information about working with estimation data types, see Data Types in System Identification Toolbox.

`data`

— Estimation data object

`iddata`

object | `frd`

object | `idfrd`

object

Estimation data object, specified as an `iddata`

object, an
`frd`

object, or an `idfrd`

object that contains
uniformly sampled input and output values. By default, the software sets the sample time
of the model to the sample time of the estimation data.

For multiexperiment data, the sample times and intersample behavior of all the experiments must match.

For time-domain estimation, `data`

must be an `iddata`

object containing the input and output signal values.

For frequency-domain estimation, `data`

can be one of the
following:

#### Limitations

You cannot estimate continuous-time models using discrete-time frequency-domain data.

`np`

— Number of poles

nonnegative integer | matrix

Number of poles in the estimated transfer function, specified as a nonnegative integer or a matrix.

For systems that have multiple inputs and/or multiple outputs, you can apply either
a global value or individual values of `np`

to the input/output
pairs, as follows:

Same number of poles for every pair — Specify

`np`

as a scalar.Individual number of poles for each pair — Specify

`np`

as an*N*-by-_{y}*N*matrix._{u}*N*is the number of outputs and_{y}*N*is the number of inputs._{u}

For an example, see Estimate Transfer Function Model by Specifying Number of Poles.

`nz`

— Number of zeros

nonnegative integer | matrix

Number of zeros in the estimated transfer function, specified as a nonnegative integer or a matrix.

For systems that have multiple inputs, multiple outputs, or both, you can apply
either a global value or individual values of `nz`

to the
input/output pairs, as follows:

Same number of poles for every pair — Specify

`nz`

as a scalar.Individual number of poles for each pair — Specify

`nz`

as an*N*-by-_{y}*N*matrix._{u}*N*is the number of outputs and_{y}*N*is the number of inputs._{u}

For a continuous-time model estimated using discrete-time data, set
`nz`

<= `np`

.

For discrete-time model estimation, specify `nz`

as the number of
zeros of the numerator polynomial of the transfer function. For example,
`tfest(tt,2,1,'Ts',data.Ts)`

estimates a transfer function of the
form $${b}_{1}{z}^{-1}/(1+{a}_{1}{z}^{-1}+{b}_{2}{z}^{-2})$$, while `tfest(tt,2,2,'Ts',data.Ts)`

estimates $$({b}_{1}{z}^{-1}+{b}_{2}{z}^{-2})/(1+{a}_{1}{z}^{-1}+{b}_{2}{z}^{-2})$$. Here, *z ^{-1}* is the
Z-transform lag variable. For more information about discrete-time transfer functions,
see Discrete-Time Representation. For an example, see Estimate Discrete-Time Transfer Function.

`iodelay`

— Transport delay

`[]`

(default) | nonnegative integer | matrix

Transport delay, specified as a nonnegative integer, a `NaN`

scalar, or a matrix.

For continuous-time systems, specify transport delays in the time unit stored in the
`TimeUnit`

property of `data`

. For discrete-time
systems, specify transport delays as integers denoting delays of a multiple of the
sample time `Ts`

.

For a MIMO system with *N _{y}* outputs and

*N*inputs, set

_{u}`iodelay`

to an
*N*-by-

_{y}*N*array. Each entry of this array is a numerical value that represents the transport delay for the corresponding input/output pair. You can also set

_{u}`iodelay`

to a scalar value to apply the same delay to all input/output pairs.The specified values are treated as fixed delays. To denote unknown transport
delays, specify `NaN`

in the `iodelay`

matrix.

Use `[]`

or `0`

to indicate no transport
delay.

For an example, see Estimate Transfer Function Containing Known Transport Delay.

`opt`

— Estimation options

`tfestOptions`

option set

Estimation options, specified as a `tfestOptions`

option set. Options specified by `opt`

include:

Estimation objective

Handling of initial conditions

Numerical search method to be used in estimation

Intersample behavior

For an example, see Estimate Transfer Function Using Estimation Option Set.

`init_sys`

— Linear system that configures initial parameterization of ` sys`

`idtf`

model | linear model | structure

Linear system that configures the initial parameterization of
`sys`

, specified as an `idtf`

model, a linear model , or a
structure. You obtain `init_sys`

either by performing an estimation
using measured data or by direct construction.

If `init_sys`

is an `idtf`

model,
`tfest`

uses the parameter values of `init_sys`

as the initial guess for estimating `sys`

.

Use the `Structure`

property of `init_sys`

to
configure initial parameter values and constraints for the numerator, denominator, and
transport lag. For instance:

To specify an initial guess for the

*A*matrix of`init_sys`

, set`init_sys.Structure.Numerator.Value`

to the initial guess.To specify constraints for the

*B*matrix of`init_sys`

:Set

`init_sys.Structure.Numerator.Minimum`

to the minimum numerator coefficient values.Set

`init_sys.Structure.Numerator.Maximum`

to the maximum numerator coefficient values.Set

`init_sys.Structure.Numerator.Free`

to indicate which numerator coefficients are free for estimation.

For an example, see Estimate Transfer Function with Unknown, Constrained Transport Delays.

If
`init_sys`

is not an `idtf`

model, the software
first converts `init_sys`

to a transfer function.
`tfest`

uses the parameters of the resulting model as the initial
guess for estimation.

If you do not specify `opt`

, and `init_sys`

was obtained by estimation rather than construction, then the software uses estimation
options from `init_sys.Report.OptionsUsed`

.

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

**Example: **`sys = tfest(data,np,nz,'Ts',0.1)`

`InputName`

— Input channel names

`" "`

(default) | string | character vector | array of strings | cell array of character vectors

Input channel names for timetable data, specified as a string, a character vector, or
an array or cell array of strings or character vectors. By default, the software
interprets all but the last variable in `tt`

as input channels. When
you want to select a subset of the timetable variables as input channels use
`'InputName'`

to identify them. For example, ```
sys =
tfest(tt,__,'InputName',["u1" "u2"])
```

selects the variables
`u1`

and `u2`

as the input channels for the
estimation.

`OutputName`

— Output signal names

`" "`

(default) | character vector | string | cell array of character vectors or strings

Output channel names for timetable data, specified as a string, a character vector, or
an array or cell array of strings or character vectors. By default, the software
interprets the last variable in `tt`

as the sole output channel. When
you want to select a subset of the timetable variables as output channels, use
`'OutputName'`

to identify them. For example, ```
sys =
tfest(tt,__,'OutputName',["y1" "y3"])
```

selects the variables
`y1`

and `y3`

as the output channels for the
estimation.

`Ts`

— Sample time of estimated model

`0`

(continuous time) (default) | positive scalar

Sample time of the estimated model, specified as either `0`

or a
positive scalar.

For continuous-time models, specify

`'Ts'`

as`0`

.For discrete-time models, specify

`'Ts'`

as the data sample time in units defined by the following:For timetable-based data — The timetable

`Time`

columnFor matrix-based data — Seconds

For data objects, such as

`iddata`

objects — The`data.TimeUnit`

property

In the discrete case,

`np`

and`nz`

refer to the number of roots of*z*for the numerator and denominator polynomials.^{-1}To obtain the data sample time for a timetable

`tt`

, use the timetable property`tt.Properties.Timestep`

.

For an example, see Estimate Discrete-Time Transfer Function.

`InputDelay`

— Input delays

`0`

(default) | scalar | vector

Input delay for each input channel, specified as a scalar or a numeric vector.

For continuous-time models, specify

`'InputDelay'`

in the time units stored in the`TimeUnit`

property.For discrete-time models, specify

`'InputDelay'`

in integer multiples of the sample time`Ts`

. For example, setting`'InputDelay'`

to`3`

specifies a delay of three sampling periods.

For a system with *N _{u}* inputs, set

`InputDelay`

to an
*N*-by-1 vector. Each entry of this vector is a numerical value that represents the input delay for the corresponding input channel.

_{u}To apply the same delay to all channels, specify `InputDelay`

as a scalar.

For an example, see Specify Model Properties of Estimated Transfer Function.

`Feedthrough`

— Feedthrough for discrete-time transfer function

`0`

(default) | `1`

| logical matrix

Feedthrough for discrete-time transfer functions, specified as a logical scalar or
an
*N _{y}*-by-

*N*logical matrix.

_{u}*N*is the number of outputs and

_{y}*N*is the number of inputs. To use the same feedthrough for all input/output channels, specify

_{u}`Feedthrough`

as a scalar.Consider a discrete-time model with two poles and three zeros:

$$H({z}^{-1})=\frac{b0+b1{z}^{-1}+b2{z}^{-2}+b3{z}^{-3}}{1+a1{z}^{-1}+a2{z}^{-2}}$$

When the model has direct feedthrough, *b0* is a free parameter
whose value is estimated along with the rest of the model parameters
*b1*, *b2*, *b3*,
*a1*, and *a2*. When the model has no feedthrough,
*b0* is fixed to zero. For an example, see Estimate Discrete-Time Transfer Function with Feedthrough.

## Output Arguments

`sys`

— Identified transfer function

`idtf`

model

Identified transfer function, returned as an `idtf`

model. This model is created using the specified model orders, delays,
and estimation options.

Information about the estimation results and options used is stored in the
`Report`

property of the model. `Report`

has the
following fields.

Report Field | Description | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

`Status` | Summary of the model status, which indicates whether the model was created by construction or obtained by estimation. | ||||||||||||||||||

`Method` | Estimation command used. | ||||||||||||||||||

`InitializeMethod` | Algorithm used to initialize the numerator and denominator for estimation of continuous-time transfer functions using time-domain data, returned as one of the following values: `'iv'` — Instrument Variable approach`'svf'` — State Variable Filters approach`'gpmf'` — Generalized Poisson Moment Functions approach`'n4sid'` — Subspace state-space estimation approach
This field is especially useful to view the algorithm used
when the | ||||||||||||||||||

`N4Weight` | Weighting matrices used in the singular-value decomposition step when
`'MOESP'` — Use the MOESP algorithm by Verhaegen.`'CVA'` — Use the canonical variate algorithm (CVA) by Larimore.`'SSARX'` — Use a subspace identification method that uses an ARX estimation-based algorithm to compute the weighting.
This field is especially useful to view the weighting
matrices used when the | ||||||||||||||||||

`N4Horizon` | Forward and backward prediction horizons used when
| ||||||||||||||||||

`InitialCondition` | Handling of initial conditions during model estimation, returned as one of the following values: `'zero'` — The initial conditions were set to zero.`'estimate'` — The initial conditions were treated as independent estimation parameters.`'backcast'` — The initial conditions were estimated using the best least squares fit.
This field is especially useful to view
how the initial conditions were handled when the | ||||||||||||||||||

`Fit` | Quantitative assessment of the estimation, returned as a structure. See Loss Function and Model Quality Metrics for more information on these quality metrics. The structure has the following fields:
| ||||||||||||||||||

`Parameters` | Estimated values of model parameters. | ||||||||||||||||||

`OptionsUsed` | Option set used for estimation. If no custom options were configured,
this is a set of default options. See | ||||||||||||||||||

`RandState` | State of the random number stream at the start of estimation. Empty,
| ||||||||||||||||||

`DataUsed` | Attributes of the data used for estimation, returned as a structure with the following fields.
| ||||||||||||||||||

`Termination` | Termination conditions for the iterative search used for prediction error minimization, returned as a structure with the following fields:
For estimation methods that do not require numerical search optimization,
the |

For more information on using `Report`

, see Estimation Report.

`ic`

— Initial conditions

`initialCondition`

object | object array of `initialCondition`

values

Estimated initial conditions, returned as an `initialCondition`

object or an object array of
`initialCondition`

values.

For a single-experiment data set,

`ic`

represents, in state-space form, the free response of the transfer function model (*A*and*C*matrices) to the estimated initial states (*x*)._{0}For a multiple-experiment data set with

*N*experiments,_{e}`ic`

is an object array of length*N*that contains one set of_{e}`initialCondition`

values for each experiment.

If `tfest`

returns `ic`

values of
`0`

and you know that you have non-zero initial conditions, set the
`'InitialCondition'`

option in `tfestOptions`

to `'estimate'`

and pass the updated option
set to `tfest`

. For
example:

opt = tfestOptions('InitialCondition','estimate') [sys,ic] = tfest(data,np,nz,opt)

`'auto'`

setting of `'InitialCondition'`

uses
the `'zero'`

method when the initial conditions have a negligible
effect on the overall estimation-error minimization process. Specifying
`'estimate'`

ensures that the software estimates values for
`ic`

.
For more information, see `initialCondition`

. For an example of using this argument, see Obtain and Apply Estimated Initial Conditions.

## Algorithms

The details of the estimation algorithms used by `tfest`

vary depending
on various factors, including the sampling of the estimated model and the estimation
data.

### Continuous-Time Transfer Function Estimation Using Time-Domain Data

**Parameter Initialization**

The estimation algorithm initializes the estimable parameters using the method
specified by the `InitializeMethod`

estimation option. The default method
is the Instrument Variable (IV) method.

The State-Variable Filters (SVF) approach and the Generalized Poisson Moment Functions
(GPMF) approach to continuous-time parameter estimation use prefiltered data [1]
[2]. The constant $$\frac{1}{\lambda}$$ in [1] and [2] corresponds to the initialization option (`InitializeOptions`

) field
`FilterTimeConstant`

. IV is the simplified refined IV method and is
called SRIVC in [3]. This method has a prefilter that is the denominator of the current model, initialized
with SVF. This prefilter is iterated up to `MaxIterations`

times, until
the model change is less than `Tolerance`

.
`MaxIterations`

and `Tolerance`

are options that you
can specify using the `InitializeOptions`

structure. The
`'n4sid'`

initialization option estimates a discrete-time model, using
the N4SID estimation algorithm, that it transforms to continuous-time using `d2c`

.

Use `tfestOptions`

to create the option set used
to estimate a transfer function.

**Parameter Update**

The initialized parameters are updated using a nonlinear least-squares search method,
specified by the `SearchMethod`

estimation option. The objective of the
search method is to minimize the weighted prediction error norm.

### Discrete-Time Transfer Function Estimation Using Time-Domain Data

### Continuous-Time Transfer Function Estimation Using Continuous-Time Frequency-Domain Data

The estimation algorithm performs the following tasks:

Perform a bilinear mapping to transform the domain (frequency grid) of the transfer function. For continuous-time models, the imaginary axis is transformed to the unit disk. For discrete-time models, the original domain unit disk is transformed to another unit disk.

Perform S-K iterations [4] to solve a nonlinear least-squares problem — Consider a multi-input single-output system. The nonlinear least-squares problem is to minimize the following loss function:

$$\underset{D,{N}_{i}}{\text{minimize}}{\displaystyle \sum _{k=1}^{{n}_{f}}{\left|W({\omega}_{k})\left(y({\omega}_{k})-{\displaystyle \sum _{i=1}^{{n}_{u}}\frac{{N}_{i}({\omega}_{k})}{D({\omega}_{k})}{u}_{i}({\omega}_{k})}\right)\right|}^{2}}$$

Here,

*W*is a frequency-dependent weight that you specify.*D*is the denominator of the transfer function model that is to be estimated, and*N*is the numerator corresponding to the_{i}*i*th input.*y*and*u*are the measured output and input data, respectively.*n*and_{f}*n*are the number of frequencies and inputs, and_{u}*w*is the frequency. Rearranging the terms gives$$\underset{D,{N}_{i}}{\text{minimize}}{\displaystyle \sum _{k=1}^{{n}_{f}}{\left|\frac{W({\omega}_{k})}{D({\omega}_{k})}\left(D({\omega}_{k})y({\omega}_{k})-{\displaystyle \sum _{i=1}^{{n}_{u}}{N}_{i}({\omega}_{k}){u}_{i}({\omega}_{k})}\right)\right|}^{2}}$$

To perform the S-K iterations, the algorithm iteratively solves

$$\underset{{D}_{m},{N}_{i,m}}{\text{minimize}}{\displaystyle \sum _{k=1}^{{n}_{f}}{\left|\frac{W({\omega}_{k})}{{D}_{m-1}({\omega}_{k})}\left({D}_{m}({\omega}_{k})y({\omega}_{k})-{\displaystyle \sum _{i=1}^{{n}_{u}}{N}_{i,m}({\omega}_{k}){u}_{i}({\omega}_{k})}\right)\right|}^{2}}$$

Here,

*m*is the current iteration, and*D*is the denominator response identified at the previous iteration. Now each step of the iteration is a linear least-squares problem, where the identified parameters capture the responses_{m-1}(ω)*D*and_{m}(ω)*N*for_{i,m}(ω)*i*= 1,2,...*n*. The iteration is initialized by choosing_{u}*D*= 1._{0}(ω)The first iteration of the algorithm identifies

*D*. The_{1}(ω)*D*and_{1}(ω)*N*polynomials are expressed in monomial basis._{i,1}(ω)The second and following iterations express the polynomials

*D*and_{m}(ω)*N*in terms of orthogonal rational basis functions on the unit disk. These basis functions have the form_{i,m}(ω)$${B}_{j,m}(\omega )=\left(\frac{\sqrt{1-{\left|{\lambda}_{j,m-1}\right|}^{2}}}{q-{\lambda}_{j,m-1}}\right){\displaystyle \prod _{r=0}^{j-1}\frac{1-{({\lambda}_{j,m-1})}^{*}q(\omega )}{q(\omega )-{\lambda}_{r,m-1}}}$$

Here,

*λ*is the_{j,m-1}*j*th pole that is identified at the previous step*m*-1 of the iteration.*λ*is the complex conjugate of_{j,m-1}^{*}*λ*, and_{j,m-1}*q*is the frequency-domain variable on the unit disk.The algorithm runs for a maximum of 20 iterations. The iterations are terminated early if the relative change in the value of the loss function is less than 0.001 in the last three iterations.

If you specify bounds on transfer function coefficients, these bounds correspond to affine constraints on the identified parameters. If you have only equality constraints (fixed transfer function coefficients), the corresponding equality constrained least-squares problem is solved algebraically. To do so, the software computes an orthogonal basis for the null space of the equality constraint matrix, and then solves the least-squares problem within this null space. If you have upper or lower bounds on transfer function coefficients, the corresponding inequality constrained least-squares problem is solved using interior-point methods.

Perform linear refinements — The S-K iterations, even when they converge, do not always yield a locally optimal solution. To find a critical point of the optimization problem that can yield a locally optimal solution, a second set of iterations are performed. The critical points are solutions to a set of nonlinear equations. The algorithm searches for a critical point by successively constructing a linear approximation to the nonlinear equations and solving the resulting linear equations in the least-squares sense. The equations follow.

Equation for the

*j*th denominator parameter:$$0=2{\displaystyle \sum _{k=1}^{{n}_{f}}\mathrm{Re}\left\{\frac{{\left|W({\omega}_{k})\right|}^{2}{B}_{j}^{*}({\omega}_{k}){\displaystyle \sum _{i=1}^{{n}_{u}}{N}_{i,m-1}^{*}({\omega}_{k}){u}_{i}^{*}({\omega}_{k})}}{{D}_{m-1}^{*}({\omega}_{k}){\left|{D}_{m-1}({\omega}_{k})\right|}^{2}}\left({D}_{m}({\omega}_{k})y({\omega}_{k})-{\displaystyle \sum _{i=1}^{{n}_{u}}{N}_{i,m}({\omega}_{k}){u}_{i}({\omega}_{k})}\right)\right\}}$$

Equation for the

*j*th numerator parameter that corresponds to input*l*:$$0=-2{\displaystyle \sum _{k=1}^{{n}_{f}}\mathrm{Re}\left\{\frac{{\left|W({\omega}_{k})\right|}^{2}{B}_{j}^{*}({\omega}_{k}){u}_{l}^{*}({\omega}_{k})}{{\left|{D}_{m-1}({\omega}_{k})\right|}^{2}}\left({D}_{m}({\omega}_{k})y({\omega}_{k})-{\displaystyle \sum _{i=1}^{{n}_{u}}{N}_{i,m}({\omega}_{k}){u}_{i}({\omega}_{k})}\right)\right\}}$$

The first iteration is started with the best solution found for the numerators

*N*and denominator_{i}*D*parameters during S-K iterations. Unlike S-K iterations, the basis functions*B*are not changed at each iteration; the iterations are performed with the basis functions that yielded the best solution in the S-K iterations. As before, the algorithm runs for a maximum of 20 iterations. The iterations are terminated early if the relative change in the value of the loss function is less than 0.001 in the last three iterations._{j}(ω)If you specify bounds on transfer function coefficients, these bounds are incorporated into the necessary optimality conditions using generalized Lagrange multipliers. The resulting constrained linear least-squares problems are solved using the same methods explained in the S-K iterations step.

Return the transfer function parameters corresponding to the optimal solution — Both the S-K and linear refinement iteration steps do not guarantee an improvement in the loss function value. The algorithm tracks the best parameter value observed during these steps, and returns these values.

Invert the bilinear mapping performed in step 1.

Perform an iterative refinement of the transfer function parameters using the nonlinear least-squares search method specified in the

`SearchMethod`

estimation option. This step is implemented in the following situations:When you specify the

`EnforceStability`

estimation option as`true`

(stability is requested), and the result of step 5 of this algorithm is an unstable model. The unstable poles are reflected inside the stability boundary and the resulting parameters are iteratively refined. For information about estimation options, see`tfestOptions`

.When you add a regularization penalty to the loss function using the

`Regularization`

estimation option. For an example about regularization, see Regularized Identification of Dynamic Systems.You estimate a continuous-time model using discrete-time data (see Discrete-Time Transfer Function Estimation Using Discrete-Time Frequency-Domain Data).

You use frequency domain input/output data to identify a multi-input model.

If you are using the estimation algorithm from R2016a or earlier (see tfest Estimation Algorithm Update) for estimating a continuous-time model using continuous-time frequency-domain data, then for continuous-time data and fixed delays, the Output-Error algorithm is used for model estimation. For continuous-time data and free delays, the state-space estimation algorithm is used. In this algorithm, the model coefficients are initialized using the N4SID estimation method. This initialization is followed by nonlinear least-squares search-based updates to minimize a weighted prediction error norm.

### Discrete-Time Transfer Function Estimation Using Discrete-Time Frequency-Domain Data

The estimation algorithm is the same as for continuous-time transfer function estimation using continuous-time frequency-domain data, except discrete-time data is used.

If you are using the estimation algorithm from R2016a or earlier (see tfest Estimation Algorithm Update), the algorithm is the same as the algorithm for discrete-time transfer function estimation using time-domain data.

**Note**

The software does not support estimation of a discrete-time transfer function using continuous-time frequency-domain data.

### Continuous-Time Transfer Function Estimation Using Discrete-Time Frequency-Domain Data

The `tfest`

command first estimates a discrete-time model from the
discrete-time data. The estimated model is then converted to a continuous-time model using
the `d2c`

command. The frequency response of the
resulting continuous-time model is then computed over the frequency grid of the estimation
data. A continuous-time model of the desired (user-specified) structure is then fit to this
frequency response. The estimation algorithm for using the frequency-response data to obtain
the continuous-time model is the same as the algorithm for continuous-time transfer function
estimation using continuous-time data.

If you are using the estimation algorithm from R2016a or earlier (see tfest Estimation Algorithm Update), the state-space estimation algorithm is used for estimating continuous-time models from discrete-time data. In this algorithm, the model coefficients are initialized using the N4SID estimation method. This initialization is followed by nonlinear least-squares search-based updates to minimize a weighted prediction error norm.

### Delay Estimation

When delay values are specified as

`NaN`

, the software uses`delayest`

to estimate them separately from the model numerator and denominator coefficients.`tfest`

then treats these delay values as fixed during the iterative update of the rest of the model. Therefore, the delay values are not iteratively updated.By default, for discrete-time data (

`Ts`

>0),`delayest`

limits the search for delays to a range of 0–30 samples. For continuous-time models, this range translates to 0–30`Ts`

time units. For continuous-time data (`Ts`

= 0),`delayest`

limits the search range to 0–10 time units. You can change these limits by first creating a template model`init_sys`

using`idtf`

and then, setting the values of`init_sys.Structure.IODelay.Minimum`

and`init_sys.Structure(i,j).IODelay.Maximum`

.For an initial model,

`init_sys`

, with:`init_sys.Structure.IODelay.Value`

specified as finite values`init_sys.Structure.IODelay.Free`

specified as`true`

the initial delay values are left unchanged.

Estimation of delays is often a difficult problem. A best practice is to assess the
presence and the value of a delay. To do so, use physical insight of the process being
modeled and functions such as `arxstruc`

, `delayest`

, and `impulseest`

. For an example of determining
input delay, see Model Structure Selection: Determining Model Order and Input Delay.

## References

[1] Garnier, H., M. Mensler, and A. Richard. “Continuous-Time Model
Identification from Sampled Data: Implementation Issues and Performance Evaluation.”
*International Journal of Control 76*, no. 13 (January 2003): 1337–57.
https://doi.org/10.1080/0020717031000149636.

[2] Ljung, Lennart. “Experiments with Identification of Continuous
Time Models.”* IFAC Proceedings Volumes* 42, no. 10 (2009): 1175–80.
https://doi.org/10.3182/20090706-3-FR-2004.00195.

[3] Young, Peter, and Anthony Jakeman. “Refined Instrumental Variable
Methods of Recursive Time-Series Analysis Part III. Extensions.”* International
Journal of Control* 31, no. 4 (April 1980): 741–64.
https://doi.org/10.1080/00207178008961080.

[4] Drmač, Z., S. Gugercin, and C. Beattie. “Quadrature-Based Vector
Fitting for Discretized H_{2} Approximation.” *SIAM Journal on
Scientific Computing* 37, no. 2 (January 2015): A625–52.
https://doi.org/10.1137/140961511.

[5] Ozdemir, Ahmet Arda, and Suat
Gumussoy. “Transfer Function Estimation in System Identification Toolbox via Vector Fitting.”
*IFAC-PapersOnLine* 50, no. 1 (July 2017): 6232–37.
https://doi.org/10.1016/j.ifacol.2017.08.1026.

## Version History

**Introduced in R2012a**

### R2022b: Time-domain estimation data is accepted in the form of timetables and matrices

Most estimation, validation, analysis, and utility functions now accept time-domain
input/output data in the form of a single timetable that contains both input and output data
or a pair of matrices that contain the input and output data separately. These functions
continue to accept `iddata`

objects as a data source as well, for
both time-domain and frequency-domain data.

### R2018a: Advanced Options are deprecated for `SearchOptions`

when `SearchMethod`

is `'lsqnonlin'`

Specification of `lsqnonlin`

- related advanced options are deprecated,
including the option to invoke parallel processing when estimating using the
`lsqnonlin`

search method, or solver, in Optimization Toolbox™.

### R2016b: `tfest`

Estimation Algorithm Update

Starting in R2016b, a new algorithm is used for performing transfer function estimation
from frequency-domain data. You are likely to see faster and more accurate results with the
new algorithm, particularly for data with dynamics over a large range of frequencies and
amplitudes. However, the estimation results might not match results from previous releases.
To perform estimation using the previous estimation algorithm, append
`'-R2016a'`

to the syntax.

For example, suppose that you are estimating a transfer function model with
`np`

poles using the frequency-domain data
`data`

.

sys = tfest(data,np)

To use the previous estimation algorithm, use the following syntax.

`sys = tfest(data,np,'-R2016a')`

## See Also

`tfestOptions`

| `idtf`

| `timetable`

| `ssest`

| `procest`

| `ar`

| `arx`

| `oe`

| `bj`

| `polyest`

| `greyest`

### Topics

- Estimate Transfer Function Models at the Command Line
- Estimate Transfer Function Models with Transport Delay to Fit Given Frequency-Response Data
- Estimate Transfer Function Models with Prior Knowledge of Model Structure and Constraints
- Apply Initial Conditions When Simulating Identified Linear Models
- Troubleshoot Frequency-Domain Identification of Transfer Function Models
- What are Transfer Function Models?
- Regularized Estimates of Model Parameters
- Estimating Models Using Frequency-Domain Data

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