MATLAB Implementation of RBF Neural Networks for Classification and Regression Tasks

This documentation presents MATLAB code for implementing Radial Basis Function (RBF) neural networks, designed to handle both classification and regression problems. The algorithm employs Gaussian basis functions and utilizes efficient training approaches including k-means clustering for center selection and least-squares methods for weight optimization. Key implementation aspects include: - Flexible network architecture configuration through adjustable hidden layer neurons - Gaussian activation functions with tunable spread parameters using MATLAB's 'newrb' or 'newrbe' functions - Efficient training process handling complex datasets through MATLAB's neural network toolbox - Cross-validation integration for model performance evaluation The code generates accurate predictions by leveraging neural network capabilities to capture nonlinear patterns in data. Results provide valuable insights into data characteristics and support further research initiatives. This RBF neural network implementation serves as a robust solution for various machine learning applications, featuring clear parameter tuning options and comprehensive error analysis capabilities. Practical applications include: - Classification tasks using pattern recognition and decision boundaries - Regression analysis for continuous value prediction - Data preprocessing and feature extraction support - Model performance comparison with other machine learning techniques