PSO-Optimized BP Neural Network for Classification

In this project, I developed a PSO-optimized BP neural network program for classification applications. The implementation follows a two-stage methodology: First, Particle Swarm Optimization (PSO) is applied to determine optimal initial weights and thresholds for the neural network. Second, these optimized parameters serve as initial values for the BP neural network, which then undergoes training using momentum and adaptive learning rate algorithms to enhance convergence and stability. The accompanying materials include the dataset and modular MATLAB functions: - tiqushuju.m: Data extraction function that parses text files to construct training samplesets - mubiao.m: Target generation function that creates expected output vectors for classification - bp.m: Baseline Backpropagation neural network implementation without PSO optimization - pso.m: Custom PSO optimization algorithm specifically designed for neural network parameter tuning - psobp.m: Integrated training function that utilizes PSO-optimized weights and thresholds as initial parameters for BP network training and testing Interestingly, comparative analysis revealed that the PSO-optimized BP network demonstrated significantly poorer training and testing performance compared to the standard BP network implementation, suggesting potential areas for algorithmic improvement in the optimization approach.