Simple Classifier Based on Error Backpropagation (BP) Neural Network

This article presents a simple classifier implemented in MATLAB using an Error Backpropagation (BP) Neural Network. Neural networks have become a prominent topic in the field of artificial intelligence, serving as computational models that mimic the human brain's neural system to learn mapping relationships between inputs and outputs. The Error Backpropagation (BP) algorithm is a training method that minimizes output errors by iteratively adjusting synaptic weights through gradient descent. In this classifier implementation, the BP algorithm propagates error signals backward through the network layers, calculating partial derivatives of the loss function with respect to each weight. Key MATLAB functions likely include feedforward computation using matrix multiplication (e.g., W*x + b), activation functions like sigmoid or ReLU, and weight updates via the chain rule. This simple classifier can be applied to problems such as image recognition, speech processing, and natural language understanding, with the MATLAB code typically involving data normalization, network architecture configuration, and training loop implementation with convergence monitoring.