PSO-Optimized RBF Neural Network Implementation

This is my independently developed PSO-optimized RBF neural network program that has undergone comprehensive testing and validation to ensure reliable operation. The implementation utilizes Particle Swarm Optimization to automatically tune RBF network parameters including center positions, widths, and connection weights. Developing such programs requires significant time and technical effort, and I appreciate your support for this work. The architecture allows for practical adaptation to various applications through configurable parameters and modular design. Key features include: swarm population initialization, fitness evaluation based on mean squared error, velocity and position updates using cognitive and social components, and radial basis function kernel optimization. This PSO-RBF hybrid approach effectively identifies optimal parameter combinations, significantly enhancing prediction accuracy and pattern recognition performance in machine learning tasks. The code structure supports easy modification of swarm size, iteration counts, and RBF network topology for different dataset characteristics. Users can adapt the activation functions and kernel parameters for specific forecasting or classification requirements.