RBF Neural Network for Regression Applications

This MATLAB program implements a Radial Basis Function (RBF) neural network specifically designed for regression problems. The implementation allows users to adapt the network to different scenarios by modifying key numerical parameters. The RBF neural network is a widely-used model that learns patterns from input data and adjusts its internal weights to generate accurate predictions. Key implementation features include: - Configurable network architecture parameters (number of hidden neurons, spread constant) - Flexible training parameters (learning rate, maximum iterations) - Customizable input-output data handling Users can modify parameters such as the spread constant of Gaussian functions, number of hidden layer neurons, and training parameters to optimize regression performance. The program includes functions for data normalization, network initialization, training using gradient descent or other optimization methods, and prediction generation. Written in MATLAB, this code can be easily executed in MATLAB environments without additional dependencies. The implementation is suitable for both research and engineering applications where regression analysis and predictive modeling are required. Typical use cases include function approximation, time series prediction, and system modeling tasks. The program structure includes: - Data preprocessing and normalization functions - RBF network initialization with random center selection - Training algorithm implementation with error monitoring - Prediction and validation modules - Performance evaluation metrics (MSE, RMSE, R-squared) This implementation provides a solid foundation for regression tasks while offering flexibility for customization and extension to specific application requirements.