Particle Swarm Optimization for RBF Network Enhancement

In our MATLAB implementation of Particle Swarm Optimization for RBF networks, we utilized PSO to optimize key parameters of the RBF neural network architecture. The optimization process involves adjusting parameters such as center positions, widths of radial basis functions, and output layer weights through iterative swarm intelligence mechanisms. Our testing framework validated that the PSO-optimized RBF network achieves significantly enhanced function approximation capabilities compared to its non-optimized counterpart.

Specifically, the PSO algorithm dynamically adjusts RBF network parameters through velocity and position update equations, where each particle represents a potential solution in the parameter space. The implementation includes fitness evaluation using mean squared error between network outputs and target values, with global and personal best positions guiding the optimization trajectory. The optimized network demonstrates improved curve fitting accuracy and higher prediction precision on test datasets, achieved through systematic parameter tuning via swarm intelligence principles.

In conclusion, the integration of Particle Swarm Optimization with RBF neural networks substantially enhances function approximation performance, making the optimized network more effective and reliable for practical applications. The MATLAB code implementation features modular design with separate functions for PSO initialization, fitness evaluation, and parameter update mechanisms, ensuring reproducible optimization results.