create a multilayer perceptron with 4 inputs and a hidden layer in matlab

main.m
% Load the input data
load('input.mat');

% Define the number of neurons in the hidden layer
hiddenLayerSize = 10;

% Create a neural network with one hidden layer using the feedforwardnet function
net = feedforwardnet(hiddenLayerSize);

% Split the data into training, validation and testing sets
net.divideParam.trainRatio = 0.7;
net.divideParam.valRatio = 0.15;
net.divideParam.testRatio = 0.15;

% Train the neural network using backpropagation
net.trainFcn = 'trainlm'; % Resilient Backpropagation with weight and bias learning rules
net.trainParam.epochs = 100; % Number of epochs
net.trainParam.goal = 0.01; % Training goal
[net,tr] = train(net,inputs,outputs);

% Test the neural network
outputs_pred = net(inputs);
errors = gsubtract(outputs,outputs_pred);
performance = perform(net,outputs,outputs_pred);

% Plot the learning progress during training
plotperform(tr);
878 chars
28 lines