یه مثال ساده می زنم که از iris دیتاست استفاده شده شما ورودی خروجی های خودتون را توش قرار بدید.
net = network(1, 2, [1; 1], [1;0], [0 0; 1 0], [0 1]);
net.adaptFcn = 'adaptwb';
net.divideFcn = 'dividerand'; %Set the divide function to dividerand (divide training data randomly).
net.performFcn = 'mse';
net.trainFcn = 'trainlm'; % set training function to trainlm (Levenberg-Marquardt backpropagation)
net.plotFcns = {'plotperform', 'plottrainstate', 'ploterrhist', 'plotconfusion', 'plotroc'};
%set Layer1
net.layers{1}.name = 'Layer 1';
net.layers{1}.dimensions = 7;
net.layers{1}.initFcn = 'initnw';
net.layers{1}.transferFcn = 'tansig';
%set Layer2
net.layers{2}.name = 'Layer 2';
net.layers{2}.dimensions = 3;
net.layers{2}.initFcn = 'initnw';
net.layers{2}.transferFcn = 'tansig';
[x,t] = iris_dataset; %load of the iris data set
net = train(net,x, t); %training
y = net(x); %prediction
view(net);
این هم روش ساده تر:
[x,t] = iris_dataset;
net = patternnet;
net = configure(net,x,t);
net = train(net,x,t); %training
view(net);
y = net(x); %predict