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

I’ve created this model by editing the codes from the toolbox. The purpose of this model is to train the network with operating data from a turbine. the data is normalized and then the target will be set according to the actual fault occurrence which tagged as “1” and during normal operation “0”. I will be comparing the result of several training function, the number of neuron, the number of layers, and activation function.

`% This script assumes these variables are defined:`

% data - input data.

% target - target data.

% load data

load data.mat;

load target.mat;

x = data;

t = target;

% Choose a Training Function

% For a list of all training functions type: help nntrain

% 'trainlm' is usually fastest.

% 'trainbr' takes longer but may be better for challenging problems.

% 'trainscg' uses less memory. NFTOOL falls back to this in low memory situations.

trainFcn = 'trainbr'; % Bayesian Regularization

% Create a Feedforward Network

hiddenLayerSize = 18;

net = feedforwardnet (hiddenLayerSize,trainFcn);

% Setup Division of Data for Training, Validation, Testing

RandStream.setGlobalStream(RandStream('mt19937ar','seed',1)); % to get constant result

net.divideFcn = 'divideblock'; % Divide targets into three sets using blocks of indices

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

%TRAINING PARAMETERS

net.trainParam.show=50; %# of ephocs in display

net.trainParam.lr=0.05; %learning rate

net.trainParam.epochs=10000; %max epochs

net.trainParam.goal=0.05^2; %training goal

net.performFcn='mse'; %Name of a network performance function %type help nnperformance

% Train the Network

[net,tr] = train(net,x,t);

% Test the Network

y = net(x);

e = gsubtract(t,y);

performance = perform(net,t,y)

% View the Network

view(net)

The questions are: Is it correct to use this code below and will it affect the function of my model?

`RandStream.setGlobalStream(RandStream('mt19937ar','seed',1)); % to get constant result`

How to add another hidden layer? How to change the activation function for each layer? What is the best plot to show the capability of the ANN model to detect the fault of the turbine earlier than existing control system. Please advise me if there are anything that can be corrected/improved.

# ANSWER

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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);

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