Particle Swarm Optimization for Radial Basis Function Neural Network Implementation Example

This article provides a detailed implementation example of Particle Swarm Optimization (PSO) for Radial Basis Function (RBF) Neural Networks. The example covers the complete algorithmic workflow, including particle initialization, velocity updates, and fitness evaluation using mean squared error. Key implementation aspects include parameter selection strategies for swarm size, inertia weight, and acceleration coefficients, along with methods for dataset preprocessing through normalization and feature scaling. The example demonstrates performance optimization techniques such as k-fold cross-validation and convergence monitoring through iteration logs. Practical code implementation details include RBF center selection using k-means clustering, Gaussian basis function computation, and weight optimization through PSO's global-best positioning. This comprehensive guide helps readers understand and apply PSO-RBF neural networks effectively for pattern recognition and regression tasks.