PSO-Optimized BP Neural Network Implementation with MATLAB Code

This discussion focuses on the application of Particle Swarm Optimization (PSO) algorithm to enhance Backpropagation (BP) neural networks, along with my personal verification experience. The MATLAB implementation involves key components such as swarm initialization, velocity updates using cognitive and social parameters, and fitness evaluation through neural network training. The PSO algorithm effectively optimizes BP network weights and thresholds by minimizing prediction errors through iterative particle position updates. I have thoroughly tested this approach with various datasets and parameter configurations, confirming its stability and convergence properties. The code structure includes main functions for PSO optimization, BP network training with gradient descent, and performance evaluation metrics. I sincerely welcome your professional guidance and suggestions to further improve this implementation and explore additional optimization techniques for neural network training.