neural network with bayesian regularization: find weights and biases and recalculate the network

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

Answer 1

[Sai Veeramachaneni]

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)

[Michael Arnold]

Thanks, that helps a lot. But i have trouble to find lines like

net.input.processFcns = { }; % Remove normalization

by my own. Have you a good tip where i can finde them? And can i see somewhere the code behind "net"?