Program for Optimizing Radial Basis Function Neural Network Parameters Using Particle Swarm Optimization

This paper presents a program that leverages particle swarm optimization (PSO) to automatically optimize parameters of radial basis function neural networks (RBFNN). The implementation enables systematic adjustment of key RBFNN parameters such as center positions, widths, and connection weights through PSO's population-based search mechanism. The algorithm initializes particle positions representing potential parameter solutions and iteratively updates them based on personal and global best positions using velocity update equations. The core implementation involves encoding RBFNN parameters into particle dimensions, where each particle's position vector corresponds to a complete parameter set. Fitness evaluation employs mean squared error between network predictions and target values. Key functions include pso_optimize() for swarm management and rbfnn_train() for network evaluation. This approach effectively handles complex parameter spaces that traditional gradient-based methods struggle with, making RBFNN more efficient when processing diverse data types. The program finds practical applications in pattern recognition, data mining, and predictive modeling tasks. By automating parameter tuning, it maximizes RBFNN's potential and provides robust solutions for various real-world problems.