Three Enhanced MATLAB Source Codes for Improved Radial Basis Function Networks

This repository presents three distinct MATLAB programs engineered to optimize radial basis function (RBF) network performance through specialized enhancements. Each program implements specific algorithmic modifications to address key aspects of RBF network development, featuring modular code structures with configurable parameters for flexible experimentation.

The first program implements advanced data preprocessing techniques specifically designed for RBF networks. The code includes normalization algorithms and outlier detection functions that transform raw input data into optimal formats for network training. Key functions include data scaling routines and feature selection mechanisms that improve network convergence rates and prediction accuracy.

The second program focuses on dynamic network architecture optimization. It implements an adaptive center selection algorithm that automatically determines optimal hidden layer neurons based on data distribution patterns. The code features clustering algorithms (K-means variant) for center initialization and iterative refinement methods for network structure adjustment, allowing users to experiment with various topology configurations through parameter tuning.

The third program provides comprehensive performance analysis capabilities through built-in evaluation metrics. It implements statistical analysis functions including mean squared error calculation, convergence tracking, and generalization ability assessment. The code includes cross-validation modules and visualization tools that generate performance graphs under varying operational conditions, enabling detailed network behavior examination.

These program suites incorporate MATLAB's neural network toolbox extensions with custom optimization algorithms, providing researchers with robust frameworks for RBF network enhancement. The modular design allows independent usage or integrated implementation, featuring detailed inline documentation for each functional component to facilitate customization and further development.