MATLAB: To optimize a neural network of multiple inputs using a genetic algorithm.

genetic algorithmneural networks

I have found the answer from the matlab team but the code is applied for a single input. I have tried to modify for 4 inputs but it was not success. Could you please give me the code to modify the below code for the 4 inputs?
The GA function requires a function handle as an input argument to which it passes a 1xN vector, where N is the number of variables in the system to be optimized.
For a neural network, the weights and biases are a Mx1 vector. These may be optimized using GA.
A function can be written to accept the network, weights and biases, inputs and targets. This function may return the mean squared error based on the outputs and the targets as GA requires a function handle that only returns a scalar value.
The following code example describes a function that returns the mean squared error for a given input of weights and biases, a network, its inputs and targets.
function mse_calc = mse_test(x, net, inputs, targets)
% 'x' contains the weights and biases vector
% in row vector form as passed to it by the
% genetic algorithm. This must be transposed
% when being set as the weights and biases
% vector for the network.
% To set the weights and biases vector to the
% one given as input
net = setwb(net, x');
% To evaluate the ouputs based on the given
% weights and biases vector
y = net(inputs);
% Calculating the mean squared error
mse_calc = sum((y-targets).^2)/length(y);
end
The following code example describes a script that sets up a basic Neural Network problem and the definition of a function handle to be passed to GA. It uses the above function to calculate the Mean Squared Error.
% INITIALIZE THE NEURAL NETWORK PROBLEM %
% inputs for the neural net
inputs = (1:10);
% targets for the neural net
targets = cos(inputs.^2);
% number of neurons
n = 2;
% create a neural network
net = feedforwardnet(n);
% configure the neural network for this dataset
net = configure(net, inputs, targets);
% create handle to the MSE_TEST function, that
% calculates MSE
h = @(x) mse_test(x, net, inputs, targets);
% Setting the Genetic Algorithms tolerance for
% minimum change in fitness function before
% terminating algorithm to 1e-8 and displaying
% each iteration's results.
ga_opts = gaoptimset('TolFun', 1e-8,'display','iter');
% PLEASE NOTE: For a feed-forward network
% with n neurons, 3n+1 quantities are required
% in the weights and biases column vector.
%
% a. n for the input weights
% b. n for the input biases
% c. n for the output weights
% d. 1 for the output bias
% running the genetic algorithm with desired options
[x_ga_opt, err_ga] = ga(h, 3*n+1, ga_opts);

Best Answer

function NMSE_calc = NMSE( wb, net, input, target)
% wb is the weights and biases row vector obtained from the genetic algorithm.
% It must be transposed when transferring the weights and biases to the network net.
 net = setwb(net, wb');
% The net output matrix is given by net(input). The corresponding error matrix is given by
 error = target - net(input);
% The mean squared error normalized by the mean target variance is
 NMSE_calc = mean(error.^2)/mean(var(target',1));
% It is independent of the scale of the target components and related to the Rsquare statistic via
% Rsquare = 1 - NMSEcalc ( see Wikipedia)
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