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

Here's an example code that creates a multilayer perceptron with 4 inputs and a hidden layer in Matlab using the neural network toolbox:

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);
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In this code, the input data is loaded and a feedforward neural network is created with one hidden layer of 10 neurons. The data is then split into training, validation, and testing sets, and the backpropagation algorithm trainlm is used to train the network with a goal of reaching a mean squared error of 0.01. Finally, the neural network is tested on the input data and the learning progress during training is plotted.

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