Optimizing RBF Neural Network Weights Using Particle Swarm Algorithm

In this paper, we implement particle swarm optimization (PSO) to refine the weight parameters of Radial Basis Function (RBF) neural networks, thereby improving their capability to approximate nonlinear functions. The PSO algorithm operates as a heuristic optimization technique inspired by collective bird flocking behavior, where particles representing potential solutions navigate through the search space to locate optimal parameters. By integrating PSO into the RBF network training process, we systematically optimize weight configurations through iterative velocity and position updates using fitness evaluation functions. This approach enables identification of superior weight arrangements that significantly enhance the network's accuracy in modeling complex nonlinear relationships. The implementation typically involves initializing particle positions with current network weights, defining fitness functions based on approximation error minimization, and executing PSO iterations until convergence criteria are met. This methodology provides substantial improvements in prediction reliability and offers foundational insights for future research in neural network architecture optimization and algorithmic enhancements.