MATLAB Implementation of Backpropagation Neural Network with Customizable Hidden Layers

This is a fundamental Backpropagation (BP) neural network implementation where you can freely customize the number of hidden layers. The program is designed for pattern recognition and predictive analysis using neural network algorithms. By increasing the number of hidden layers, you can enhance the network's complexity and learning capacity, enabling better adaptation to diverse input data patterns. The implementation typically includes key MATLAB functions such as: - Network initialization with configurable layer architecture using 'feedforwardnet' or custom weight matrices - Training process using gradient descent optimization with adjustable learning rates ('trainlm' or 'traingd') - Forward propagation calculations with activation functions (sigmoid/tanh) - Backward error propagation for weight updates You can further optimize network performance by tuning additional parameters like learning rate, iteration count (epochs), and activation functions. The code structure allows modification of training parameters through 'net.trainParam' settings and supports performance visualization using MATLAB's plotting tools. Neural networks serve as powerful computational tools applicable across various domains including image recognition, natural language processing, and financial forecasting. Mastering this fundamental BP neural network implementation opens opportunities for tackling complex pattern recognition challenges and developing advanced predictive models.