MATLAB: Neural network with bayesian regularization: find weights and biases and recalculate the network

bayesian regularizationbiasesneural networkweights

Hey,
i´m trying to use a neural network to guess functional values for unknown points. This is my current solution.
%target f(x)=(x^2 + 22*x - 100)/(4*x)
%for x = [2,9]
inputall = 2:0.01:9;
outputall = (inputall.^2+22*inputall-100)./(4*inputall);
%training data
inputtrain = 2:1:9; 
outputtrain = (inputtrain.^2+22*inputtrain-100)./(4*inputtrain);
%neural network
neurons = 5; 
net = feedforwardnet(neurons,'trainbr');    
net = train(net,inputtrain,outputtrain);
%prediction
predict(1,:) = net(inputall); 
%comparison
comp = [outputall' predict']
%visualization
figure('Name','comparison'); hold on;
plot(inputall,outputall);
plot(inputall,predict)
Now I want to know what weights and biases the network finaly used. How can i get them and is it possible to use them to recalculate by myself the solution of the network?
Best regards
Michael

Best Answer

Hi,
You can use net.IW, net.LW, net.b properties of neural network object to get weights and biases used in the network.
You can use this as a reference to calculate solution using the constructed network.
References:
Neural Network Object Properties - MATLAB & Simulink (mathworks.com)
[Neural network] How does neural network calculate output from net.IW, net.LW, net.b ? - MATLAB Answers - MATLAB Central (mathworks.com)
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