Source Code Implementation of RBF Neural Network Algorithm Optimized Using Particle Swarm Optimization

This source code implements the optimization of RBF neural network algorithms using Particle Swarm Optimization (PSO) to enhance computational performance and prediction accuracy. The PSO algorithm is a population-based optimization technique inspired by collective behaviors observed in bird flocks or fish schools, designed to efficiently locate optimal solutions in complex search spaces. By integrating PSO with RBF neural networks, the hybrid approach significantly improves the algorithm's learning efficiency and generalization capabilities through strategic parameter optimization. Key implementation aspects include: 1) PSO-based optimization of RBF center selection and weight initialization 2) Adaptive adjustment of network parameters using swarm intelligence 3) Fitness function design targeting error minimization and convergence acceleration. The optimized source code enhances computational efficiency and result precision through intelligent parameter tuning and convergence mechanisms, making it particularly suitable for practical applications requiring robust pattern recognition and function approximation capabilities.