Predicting if a time-series nonlinear signal will reach end positions( 0 or max)

Hello All, I am trying to build a Matlab/Simulink model to predict if a signal would reach end values. However the incoming data is realtime/dynamic , I have less control over the past data(only a few samples to say) in order to avoid more delay in the control system.

Can this done using Neural Nets considering less delay/few past samples? I went through some answers related to market trends here but seemed complex. Could a simple NN model be built with limited(Sorry,I am beginner to NN) or any simple method is possible ?

Please any idea or direction would be helpful. Thank you in advance

Note:In realtime data is NON LINEAR/EXPONENTIAL.

[Greg Heath]

Why don't you pick a dataset from MATLAB's NN example library?

 help nndatasets

 doc nndatasets

Greg

[Pranav]

Hi Greg, Thank you for the reply.

I am using simplenar_dataset model given one only time-series to predict the next value of exponential curve. Is there a time delay introduced using this model.

What could be least number of previous samples used without/less delay to predict? In the example , it takes 100 samples which is too high for my application.

Answer 1

[Greg Heath]

The answer to questions you have about input and feedback delays can be answered if you have relevant design data by using

 1. SIGNIFICANT DELAYS OF THE INPUT-TARGET CROSS-CORRELATION 
    FUNCTION

 2.  SIGNIFICANT DELAYS OF THE TARGET AUTO-CORRELATION FUNCTION

 3. If you use the NN Toolbox function NNCORR

    help NNCORR

    doc NNCORR

   It will be worthwhile to see some of my NEWSGROUP and ANSWERS 
   posts. Search with

    greg nncorr

Hope this helps.

Thank you for formally accepting my answer

Greg