I have found the answer from the Matlab team but the code is applied for a single input.
I need to get 3 inputs of 43 rows from excel and 1 output of 43 rows from excel should be trained by neural network and should be applied using genetic algorithm to find the mean squared error.
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);endThe 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