Optimizing RBF Network Weights Using Particle Swarm Optimization

In this paper, we propose using Particle Swarm Optimization (PSO) to optimize the weights of Radial Basis Function (RBF) networks. The optimization process involves initializing particle positions representing weight vectors, where each particle's velocity and position are iteratively updated according to its personal best (pbest) and the global best (gbest) positions. The fitness function typically calculates the mean squared error between the RBF network's predictions and actual targets. Through fitness-based updates of individual and swarm optimal positions, we can significantly enhance the algorithm's convergence performance and optimization effectiveness. This method enables more accurate and efficient problem-solving in function approximation and pattern recognition tasks. The implementation typically requires coding the PSO main loop with weight initialization, RBF network forward propagation, and fitness evaluation at each iteration. Therefore, applying PSO to optimize RBF network weights represents a promising research direction with substantial potential. Future studies could explore hybrid optimization algorithms, adaptive parameter tuning strategies, or parallel computing implementations to achieve superior results and performance. We believe that through continued research efforts, significant breakthroughs and advancements can be made in optimizing RBF network weight parameters.