A Multilayer Perceptron Implementation in MATLAB with Neural Network Applications

In this article, we provide a comprehensive explanation of a multilayer perceptron implemented in MATLAB. A multilayer perceptron, also known as an artificial neural network, serves as a powerful machine learning algorithm applicable to classification, regression, and clustering tasks. The architecture consists of multiple interconnected neurons, where each neuron processes multiple inputs to generate a single output through activation functions like sigmoid or ReLU. The network autonomously learns intricate relationships between input and output data through backpropagation optimization, enabling accurate predictions for new datasets. Our discussion covers fundamental concepts, working principles, and implementation methodologies, supplemented with practical MATLAB code snippets demonstrating weight initialization, forward propagation, loss calculation, and gradient descent updates. Key functions such as feedforwardnet() and train() will be examined alongside custom implementations for finer control over network parameters. Ultimately, this guide aims to deliver actionable insights and structured approaches for readers seeking to master multilayer perceptron development and application in MATLAB environments.