Practical Application Example of Momentum-Adaptive Learning Rate Adjustment Algorithm (Improved BP Algorithm)

In this article, we share a MATLAB implementation example demonstrating the momentum-adaptive learning rate adjustment algorithm. This algorithm represents an improved version of the backpropagation method, specifically designed to enhance the optimization of weights and biases in neural networks. Our implementation focuses on training a neural network for handwritten digit classification. We'll explain the core algorithm principles and workflow while providing sample code segments to illustrate key implementation aspects. The code demonstrates how to initialize network parameters, implement the adaptive learning mechanism with momentum term, and calculate gradient updates efficiently. We'll examine how different parameters - such as initial learning rates, momentum coefficients, and convergence thresholds - affect neural network performance. The implementation includes practical debugging techniques like gradient checking and loss visualization. Key MATLAB functions involved include customized training loops, activation function implementations, and precision evaluation metrics. Through this example, you'll learn to properly apply the momentum-adaptive learning algorithm and achieve more accurate classification results in neural networks. The code structure emphasizes modular design for easy parameter experimentation and performance optimization.