Artificial neural networks Day 02

I. Perceptron model using MATLAB to solve linear classification problems.

The following shows steps to create a Perceptron Neural Network model to solve linear classification problems using MATLAB.

1. Define the input e.g. x[0 0 1 1;0 1 0 1];

2. Define the target e.g. t=[0 0 0 1]; %  This target shows that we are solving the AND problem.

3. Define the network architecture i.e. net=perceptron;

OR i.e. net = newp([0 1;0 1],[0 1]); % That is, inputs will be 2-element column vectors in which values for each element are 0 or 1, and output will be 1-element "vector" that has values of 0 or 1.

4. Train the network i.e. net= train(net,x,t);

5. View the network i.e. view(net);

6  Simulate the network with a new input to obtain the output e.g. y=sim(net,[0;0]) or y=sim(net,[0;1]).

In step 3 above, if the "newp" function is used, we can set the initial set of weights, bias, learning parameter and other parameters (e.g. number of epochs) as follows.

3a. Set initial set of weights and bias

For example,

w = [1 -0.8]; %i.e. w1=1, w2=-0.8

net.IW{1,1} = w;

net.b{1} =  [0]; %i.e. w0=0

3b. Set learning rate

net.trainParam.lr=0.1;

3c. Set the number of epochs for training

net.trainParam.epochs=10;

7. Find out the final weights and bias after training the network.

w = net.iw{1,1}

b = net.b{1}

Note: patternnet should be used to solve non-linearly separable problems.

II. Perceptron architecture

References
MATLAB 2010b Help Documentation