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- input_len = length(input);
- shuffled_array = randperm(input_len);
- input = input(shuffled_array,:);
- output = output(shuffled_array,:);
- %disp(output);
- net = feedforwardnet([3 2]);
- %net = feedforwardnet([4 3]);
- net.layers{3}.transferFcn = 'logsig';
- % podesavanje parametara za treniranje
- net.divideParam.trainRatio = 0.8; % training set [%]
- net.divideParam.valRatio = 0; % validation set [%]
- net.divideParam.testRatio = 0.2; % test s
- % neural network training
- [net,tr,Y,E] = train(net,input',output');
- netoutput = sim(net, input');
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