Neural Network Predictive Control

The following source code implements a Neural Network Predictive Control system. This program utilizes Radial Basis Function (RBF) neural networks for prediction and control tasks. RBF neural networks are widely recognized for their excellent approximation capabilities and rapid convergence properties, making them particularly suitable for real-time control applications. The neural network model in this implementation undergoes comprehensive training and optimization processes to ensure accurate predictions and stable control performance.

The codebase includes robust data preprocessing modules that normalize input data and handle missing values, feature extraction components that identify relevant patterns from time-series data, and result analysis functionalities that evaluate control performance through metrics like mean squared error and stability indices. Key algorithmic components include gradient-based learning methods for network weight updates, recursive prediction algorithms for multi-step ahead forecasting, and constraint handling mechanisms for practical control implementations.

By leveraging this program, engineers and researchers can effectively apply neural network predictive control techniques to solve complex industrial control problems, achieving superior control performance through adaptive learning capabilities and optimized prediction models.