Particle Swarm Optimization Algorithm for RBF Neural Network Enhancement

The PSO-optimized RBF Neural Network algorithm presented in this paper implements the following optimization workflow: 1. Perform sample data normalization by scaling both input and output values to the [-1,1] interval using min-max scaling techniques. This preprocessing step ensures consistent data ranges for neural network training. 2. Determine optimal centers and widths for the Radial Basis Function network. This can be implemented using clustering algorithms like K-means for center selection and Gaussian width calculation based on inter-cluster distances. 3. Utilize Particle Swarm Optimization with root mean square error (RMSE) of fitting accuracy as the fitness function to optimize the connection weight matrix between the RBF network's output layer and hidden layer. The PSO implementation typically involves position updates using velocity vectors and personal/global best positions. To achieve better optimization performance, consider these additional enhancements: - Before data normalization, implement data cleaning and preprocessing routines to ensure data accuracy and consistency, including handling missing values and outlier detection. - When determining RBF network centers and widths, incorporate alternative optimization algorithms such as Genetic Algorithms or Simulated Annealing to explore optimal parameter combinations through evolutionary computing approaches. - During PSO optimization, configure appropriate iteration limits and convergence criteria (e.g., tolerance thresholds or stagnation detection) to achieve superior optimization results while preventing unnecessary computational overhead.