How to change the activation function in ANN model created using toolbox

Izaizi Ismail asked: 2021-08-04 neural networks , activation functions

Learn how to change the activation function in your MATLAB Artificial Neural Network (ANN) model created using the toolbox. Step-by-step guide & code examples i

Expert Answer

Kshitij Singh answered . 2025-08-18 20:14:41

You are approaching the problem in exactly the wrong way.

The multilayer perceptron with one hidden layer is a universal approximator. The only reason to use more than one hidden layer is to reduce the total number of unknown weights by reducing the total number of hidden nodes (i.e., H1+H2 < H).

Ntrn training pairs of I-dimensional inputs and O-dimensional output targets yields Ntrneq = Ntrn*O training equations. The best way to obtain a robust design that tends to be resistant to noise, interference, measurement and transcription error is to MINIMIZE the number of unknown weights that yield an acceptable solution. If possible, Nw << Ntrneq is desirable

 1. Use FITNET (calls FEEDFORWARDNET) for regression and curve-fitting
 2. Use PATTERNNET (calls FEEDFORWARDNET) for classification and pattern-recognition
 3. You have a classification problem. Start with the simple code in
     help patternnet
     doc patternnet
 4. If there are c classes, the target matrix columns should be columns of eye(c): O = c.
 5. The relationship between trueclass indices 1:c and the target columns is
 target           = ind2vec(trueclassindices);
 trueclassindices = vec2ind(target);

 6. Before starting the design, get a "feel" for the data. This may include
  a. plot inputs
  b. plot targets
  c. plot targets vs inputs
  d. standardize inputs to zero mean and unit variance using zscore or mapstd.
  e. Repeat a and c 
  f. Remove or modify errors and outliers.

 7. Start simple with the example used in the help and doc documentation.
  help patternnet
  doc patternnet

 8. You only have to vary 2 things 
  a. Number of hidden nodes (want as small as feasible) 
  b. Initial random weights

 9. This can be accomplished with a double for loop as I have illustrated in zillions of examples in the NEWSGROUP and ANSWERS. Search results

 NEWSGROUP HITS
 greg patternnet Ntrials  8
 ANSWERS HITS
 greg patternnet Ntrials           60
 greg patternnet Ntrials  Hmax     22
 greg patternnet Ntrials  Hub      17
 greg patternnet Ntrials  Hub Hmax 10