MATLAB Examples

Fit an exponential model to data using the fit function.

Fit and compare polynomials up to sixth degree using Curve Fitting Toolbox, fitting some census data. It also shows how to fit a single-term exponential equation and compare this to the

The aim of this analysis is to characterize the dose response behavior of 4 different drug candidates in a population. The objective of this analysis is investigate the how the treatments

This demo is an example of performing data mining on historical fuel economy data. We have data from various cars built from year 2000 up to 2012.

Copyright 2016 The MathWorks, Inc.Published with MATLAB® R2016a

Goal - Produce a reliable med term forecasting model for Energy Demand

This Spectr-O-Matic example decomposes a mixture spectrum into reference components by linear least squares fit.

This example was authored by the MathWorks community.

Use the fit function to fit polynomials to data. The steps fit and plot polynomial curves and a surface, specify fit options, return goodness of fit statistics, calculate predictions, and

In this demo, we use regression trees to predict the fuel economy of vehicles.

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緯度

The aim of this demo is to characterize the "complete spectrum of interaction" between opiods and hypnotics, using propofol and remifentanil as drug class prototypes [1]. 4 different

Work with a curve fit.

Create specdata objects from variables

Example script for Spectr-O-matic toolbox.

Use the fit function to fit a Fourier model to data.

Compare the effects of excluding outliers and robust fitting. The example shows how to exclude outliers at an arbitrary distance greater than 1.5 standard deviations from the model. The

Fit a custom equation to census data, specifying bounds, coefficients, and a problem-dependent parameter.

Work with a surface fit.

Find the first and second derivatives of a fit, and the integral of the fit, at the predictor values.

Copyright 2017 - 2017 The MathWorks, Inc.

This script contains the examples shown in the webinar titled Optimization Tips and Tricks: Getting Started using Optimization with MATLAB presented live on 21 August 2008. To view the

Simulates the movements of a swarm to minimize the objective function

Control vector parameterization, also known as direct sequential method, is one of the direct optimization methods for solving optimal control problems. The basic idea of direct

This is a simple Evolutionary Multiobjective Optimization problem (two objectives).

The purpose of this demo is to reconstruct a simple picture of several polygons. I start by generating 'numOfPolygons' polygons of random colors ( left upper corner in the figure), say it's

Fit a function to data using lsqcurvefit together with MultiStart .

Find the minimum of Rastrigin's function restricted so the first component of x is an integer. The components of x are further restricted to be in the region 5 \pi\le x(1) \le 20\pi,\ -20\pi\le

Optimize using the particleswarm solver. The particle swarm algorithm moves a population of particles called a swarm toward a minimum of an objective function. The velocity of each

Use an output function for particleswarm. The output function plots the range that the particles occupy in each dimension.

Optimize using the particleswarm solver.

How @gacreationlinearfeasible, the default creation function for linearly constrained problems, creates a population for ga. The population is well-dispersed, and is biased to lie on

Solve a mixed integer engineering design problem using the Genetic Algorithm (ga) solver in Global Optimization Toolbox.

The use of a custom output function in the genetic algorithm solver ga. The custom output function performs the following tasks:

Minimize an objective function subject to nonlinear inequality constraints and bounds using the Genetic Algorithm.

Use the genetic algorithm to minimize a function using a custom data type. The genetic algorithm is customized to solve the traveling salesman problem.

Create and manage options for the genetic algorithm function ga using optimoptions in the Global Optimization Toolbox.

Use a hybrid scheme to optimize a function using the Genetic Algorithm and another optimization method. ga can reach the region near an optimum point relatively quickly, but it can take many

Create and minimize a fitness function using the Genetic Algorithm in the Global Optimization Toolbox.

Create and minimize an objective function using Simulated Annealing in the Global Optimization Toolbox.

Create and manage options for the simulated annealing function simulannealbnd using optimoptions in the Global Optimization Toolbox.

Use simulated annealing to minimize a function using a custom data type. Here simulated annealing is customized to solve the multiprocessor scheduling problem.

Use the functions GlobalSearch and MultiStart.

State Space MPC code.

This document explains how to use the state space MPC function which using input increment.

The Padé approximant often gives better approximation of the function than truncating its Taylor series, and it may still work where the Taylor series does not converge. For these reasons

Teaches how to use the Metropolis algorithm to simulate the Ising model of a ferromagnet in MATLAB.

Center for Open Data in Humanities launched Japanese Classics Character Dataset in November 2016 [1]. This is a large dataset of various hand-written characters from classical documents

X_s=sym('x_s'); y_s= 2/(1+exp(-2*x_s))-1; %Eqn of hyperbolic tangent, from apply_transfer dy_s=diff(y_s,x_s); % Put into apply_transfer of modified file ddy_s=diff(dy_s,x_s); %

Illustrates how a self-organizing map neural network can cluster iris flowers into classes topologically, providing insight into the types of flowers and a useful tool for further

Demonstrates looking for patterns in gene expression profiles in baker's yeast using neural networks.

Neurons in a competitive layer learn to represent different regions of the input space where input vectors occur.

Neurons in a 2-D layer learn to represent different regions of the input space where input vectors occur. In addition, neighboring neurons learn to respond to similar inputs, thus the layer

As in one-dimensional problems, this self-organizing map will learn to represent different regions of the input space where input vectors occur. In this example, however, the neurons will

Illustrates how a pattern recognition neural network can classify wines by winery based on its chemical characteristics.

Illustrates how to train a neural network to perform simple character recognition.

Use Neural Network Toolbox™ autoencoders functionality for training a deep neural network to classify images of digits.

Illustrates using a neural network as a classifier to identify the sex of crabs from physical dimensions of the crab.

Demonstrates using a neural network to detect cancer from mass spectrometry data on protein profiles.

Illustrates how a NARX (Nonlinear AutoRegressive with eXternal input) neural network can model a magnet levitation dynamical system.

Illustrates how a function fitting neural network can estimate body fat percentage based on anatomical measurements.

Illustrates how to design a linear neuron to predict the next value in a time series given the last five values.

Illustrates how an adaptive linear layer can learn to predict the next value in a signal, given the current and last four values.

A 2-input hard limit neuron fails to properly classify 5 input vectors because they are linearly non-separable.

A 2-input hard limit neuron is trained to classify 5 input vectors into two categories.

A 2-input hard limit neuron is trained to classify 5 input vectors into two categories. Despite the fact that one input vector is much bigger than the others, training with LEARNPN is quick.

A 2-input hard limit neuron is trained to classify 5 input vectors into two categories. However, because 1 input vector is much larger than all of the others, training takes a long time.

Uses functions NEWPNN and SIM.

This examples illustrates how to perform a FORM analysis on a discrete (0 or 1) failure response. In the example we'll compare a traditional Monte Carlo method with FORM. This example is was

We propose two fuzzy portfolio optimization models based on the Markowitz Mean-Variance approach. The first model involves trapezoidal fuzzy numbers to extent statistical data, which

This demo was adapted from a 2009 digest article: Improving Optimization Performance with Parallel Computing

Time series of acceleration records are simulated using a stationnary process that is "weighted" by an envelopp function. The function that fullfills this procedure is 'seismSim'.

This code is an applicatino of EMOO by using Genetic algorithms to solve the following simple constrained problem: Draw the biggest possible circle in a 2D space filled with stars without

Solve portfolio optimization problems using the interior-point quadratic programming algorithm in quadprog. The function quadprog belongs to Optimization Toolbox™.

Determine the shape of a circus tent by solving a large-scale quadratic optimization problem. The shape of a circus tent is determined by a constrained optimization problem. We will solve

The value of using sparse arithmetic when you have a sparse problem. The matrix has n rows, where you choose n to be a large value, and a few nonzero diagonal bands. A full matrix of size n -by- n can

Use binary integer programming to solve the classic traveling salesman problem. This problem involves finding the shortest closed tour (path) through a set of stops (cities). In this case

Set up and solve a mixed-integer linear programming problem. The problem is to find the optimal production and distribution levels among a set of factories, warehouses, and sales outlets.

Schedule two gas-fired electric generators optimally, meaning to get the most revenue minus cost. While the example is not entirely realistic, it does show how to take into account costs

Solve a Sudoku puzzle using binary integer programming. For the solver-based approach, see Solve Sudoku Puzzles Via Integer Programming: Solver-Based .

Solve a Mixed-Integer Quadratic Programming (MIQP) portfolio optimization problem using the problem-based approach. The idea is to iteratively solve a sequence of mixed-integer linear

Solve an assignment problem by binary integer programming using the optimization problem approach. For the solver-based approach, see Office Assignments by Binary Integer Programming:

Solve a cutting stock problem using linear programming with an integer linear programming subroutine. The example uses the problem-based approach. For the solver-based approach, see

How to speed up the minimization of an expensive optimization problem using functions in Optimization Toolbox™ and Global Optimization Toolbox. In the first part of the example we solve the

Use two nonlinear optimization solvers and how to set options. The nonlinear solvers that we use in this example are fminunc and fmincon .

Perform nonlinear fitting of complex-valued data. While most Optimization Toolbox™ solvers and algorithms operate only on real-valued data, least-squares solvers and fsolve can work on

Analyze an idealized 3-D mechanical part under an applied loading using Finite Element Analysis (FEA). The objective of the analysis is to determine the maximum deflection caused by the

This examples conducts a parametric study in which heat conduction simulation is performed over a set of similar geometries to determine which geometry "best" meets an average temperature

Calculate the deflection of a structural plate acted on by a pressure loading using the Partial Differential Equation Toolbox™.

Solve Poisson's equation using the programmatic workflow. For the PDE Modeler app solution, see docid:pde_ug.bvhf75n . The problem formulation is - \Delta u = 1 in \Omega , u = 0 on \delta

Solve the heat equation with a source term using the Partial Differential Equation Toolbox™.

How a 3-D axisymmetric model can be analyzed using a 2-D model. The model geometry, material properties, and boundary conditions must all be symmetric about a single axis for this

Perform a heat transfer analysis of a thin plate using the Partial Differential Equation Toolbox™.

An idealized thermal analysis of a rectangular block with a rectangular cavity in the center. One of the purposes of this example is to show how temperature-dependent thermal conductivity

Perform a 3-D transient heat conduction analysis of a hollow sphere made of three different layers of material, subject to a nonuniform external heat flux.

Solve a coupled elasticity-electrostatics problem using Partial Differential Equation Toolbox™. Piezoelectric materials deform when a voltage is applied. Conversely, a voltage is

Include damping in the transient analysis of a simple cantilever beam analyzed with the Partial Differential Equation Toolbox™. The beam is modeled with a plane stress elasticity

Analyze an idealized 3-D mechanical part under an applied load using Finite Element Analysis (FEA). The objective of the analysis is to determine the maximal deflection caused by the load.

Calculate the vibration modes and frequencies of a 3-D simply supported, square, elastic plate. The dimensions and material properties of the plate are taken from a standard finite element

The Partial Differential Equation Toolbox™ analysis of the dynamic behavior of a beam clamped at both ends and loaded with a uniform pressure load. The pressure load is suddenly applied at

Perform modal and transient analysis of a tuning fork.

Perform a 2-D plane-stress elasticity analysis.

Create contour slices in various directions through a solution in 3-D geometry.

Solves a Poisson's equation with a delta-function point source on the unit disk using the adaptmesh function in the Partial Differential Equation Toolbox™.

Numerically solve a Poisson's equation using the solvepde function in Partial Differential Equation Toolbox™.

Solve the wave equation using the solvepde function in the Partial Differential Equation Toolbox™.

Solve a Helmholtz equation using the solvepde function in Partial Differential Toolbox™.

Use anovan to fit models where a factor's levels represent a random selection from a larger (infinite) set of possible levels.

Machine learning techniques are often used for financial analysis and decision-making tasks such as accurate forecasting, classification of risk, estimating probabilities of default,

Human activity sensor data contains observations derived from sensor measurements taken from smartphones worn by people while doing different activities (walking, lying, sitting etc).

This demo showcases visualization and analysis (heavy statistics) for forecasting energy usage based on historical data. We have access to hour-by-hour utility usage for the month of

Clustering is a form of unsupervised learning technique. The purpose of clustering is to identify natural groupings of data from a large data set to produce a concise representation based on

Demonstrates fitting a non-linear temperature model to hourly dry bulb temperatures recorded in the New England region. The temperature series is modeled as a sum of two compoments, a

Generate a nonlinear classifier with Gaussian kernel function. First, generate one class of points inside the unit disk in two dimensions, and another class of points in the annulus from

In this example, use a database of 1985 car imports with 205 observations, 25 predictors, and 1 response, which is insurance risk rating, or "symboling." The first 15 variables are numeric

Demonstration of dot product, orthogonality also includes some vector addition. Information from this tutorial is used in qr decomposition and multiple regression regression approach

Compute and plot the pdf of a Poisson distribution with parameter lambda = 5 .

Use Cook's Distance to determine the outliers in the data.

Principal Component Analysis (PCA) and Partial Least Squares (PLS) are widely used tools. This code is to show their relationship through the Nonlinear Iterative PArtial Least Squares

Use copulafit to calibrate copulas with data. To generate data Xsim with a distribution "just like" (in terms of marginal distributions and correlations) the distribution of data in the

Perform linear and quadratic classification of Fisher iris data.

Similar to the bootstrap is the jackknife, which uses resampling to estimate the bias of a sample statistic. Sometimes it is also used to estimate standard error of the sample statistic. The

Linstats package provides a uniform mechanism for building any supported linear model. Once built the same model can be analyzed in many ways including least-squares regression, fit and

Find the indices of the three nearest observations in X to each observation in Y with respect to the chi-square distance. This distance metric is used in correspondence analysis,

Perform N-way ANOVA on car data with mileage and other information on 406 cars made between 1970 and 1982.

Plot the pdf of a bivariate Student's t distribution. You can use this distribution for a higher number of dimensions as well, although visualization is not easy.

Linear Mixed-Effect (LME) Models are generalizations of linear regression models for data that is collected and summarized in groups. Linear Mixed- Effects models offer a flexible

Compute and plot the pdf using four different values for the parameter r , the desired number of successes: .1 , 1 , 3 , and 6 . In each case, the probability of success p is .5 .

This script demonstrates using the included Talbot and Euler algorithms for numerical approximations of the inverse Laplace transform. The examples cover functions with known inverses

This code solves a test problem involving a Poisson equation on a square domain. The method relies on Lagrangian finite elements on a uniform triangular mesh. The solver is documented in the

INTRODUCTION

This code solves the test problem of a thermally driven flow in a rectangular enclosure with an aspect ration of 8:1, as described in Christon et al. (2002). The method relies on Taylor-Hood

This code solves a test problem involving a Burgers equation on a square domain, described in "Singler (2014). The method relies on linear Lagrangian finite elements on a uniform triangular

This file contains an explanation of the difference between implicit and explicit time integration schemes. The content is intended for those who want to learn a bit more than what the

Use functional derivatives in the Symbolic Math Toolbox™ using the example of the wave equation. The wave equation for a string fixed at its ends is solved using functional derivatives. A

Do rotations and transforms in 3D using Symbolic Math Toolbox™ and matrices.

Perform simple matrix computations using Symbolic Math Toolbox™.

Compute the inverse of a Hilbert matrix using Symbolic Math Toolbox™.

Solve the eigenvalue problem of the Laplace operator on an L-shaped region.

Derive the symbolic stationary distribution of a trivial Markov chain by computing its eigen decomposition.

Work with large integers and their decimal representation using the Symbolic Math Toolbox™.

Use variable-precision arithmetic to investigate the decimal digits of pi using Symbolic Math Toolbox™.

Use variable-precision arithmetic to obtain high precision computations using Symbolic Math Toolbox™.

Get precise values for binomial coefficients and find probabilities in coin-tossing experiments using the Symbolic Math Toolbox.

Use some elementary functions on sym objects using the Symbolic Math Toolbox™.

Extracts closed-form solutions for the coefficients of frequencies in an output signal. The output signal results from passing an input through an analytical nonlinear transfer

Finds the average radiation power of two attracting charges moving in an elliptical orbit (an electric dipole ).

Develops a mathematical model using the Symbolic Math Toolbox to undistort an image and features a local function in the live script.

Uses Symbolic Math Toolbox and the Statistics and Machine Learning Toolbox to explore and derive a parametric analytical expression for the average power generated by a wind turbine.

Obtains the partial differential equation that describes the expected final price of an asset whose price is a stochastic process given by a stochastic differential equation.

Classify text descriptions of weather reports using a deep learning long short-term memory (LSTM) network.

Analyze text using n-gram frequency counts.

Use the Latent Dirichlet Allocation (LDA) topic model to analyze text data.

Create a function which cleans and preprocesses text data for analysis.

Train a simple text classifier on word frequency counts using a bag-of-words model.

Visualize text data using word clouds.

Visualize word embeddings using 2-D and 3-D t-SNE and text scatter plots.

Extract the text data from text, HTML, Microsoft® Word, PDF, CSV, and Microsoft Excel® files and import it into MATLAB for analysis.

Decide on a suitable number of topics for a latent Dirichlet allocation (LDA) model.

Compare latent Dirichlet allocation (LDA) solvers by comparing the goodness of fit and the time taken to fit the model.

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