RBF Neural Network Implementation for Predictive Modeling

This compressed file contains a complete implementation of a Radial Basis Function (RBF) neural network for predictive modeling applications. The RBF neural network employs a three-layer architecture consisting of input, hidden radial basis layer, and output layer, utilizing Gaussian functions as activation nodes for nonlinear pattern recognition. Key algorithmic features include center selection through k-means clustering and width parameter optimization using nearest neighbor methods. The program demonstrates practical implementation of gradient descent learning for weight adjustment between hidden and output layers. Potential applications span multiple domains including financial forecasting (stock price prediction), engineering systems (fault detection), and medical diagnostics (pattern classification). The code structure supports flexible data input formats (CSV, Excel, or text-based datasets) with built-in normalization preprocessing. For optimal usage, users should prepare training datasets with clear input-output mappings, specify appropriate RBF network parameters (number of centers, learning rate), and utilize the included visualization module for performance evaluation. The implementation includes cross-validation functionality to prevent overfitting and ensure robust prediction accuracy across various data types and problem domains.