How can I use the trained weights of a neural network

How can I use the trained weights of a neural network to simulate the output of a classification problem?

Answer 1

[David Franco]

I found the answer (output and yy are equal):

% Simulated a Neural Network

clear
close
clc

rng default

[x,y] = simpleclass_dataset;

neurons = 10;
net = feedforwardnet(neurons);
net = train(net,x,y);
outputs = sim(net,x);
% outputs = round(outputs);

figure, plotconfusion(y,outputs)

w1 = net.IW{1,1};
w2 = net.LW{2,1};
b1 = net.b{1};
b2 = net.b{2};

xx = x;

for ii = 1:length(net.inputs{1}.processFcns)
    xx = feval(net.inputs{1}.processFcns{ii},...
        'apply',xx,net.inputs{1}.processSettings{ii});
end

a1 = tanh(w1*xx + b1);
yy = purelin(w2*a1 + b2);

for mm = 1:length(net.outputs{1,2}.processFcns)
    yy = feval(net.outputs{1,2}.processFcns{mm},...
        'reverse',yy,net.outputs{1,2}.processSettings{mm});
end

figure, plotconfusion(y,yy)

=)