RBF Neural Network Predictive Control

In this article, we explore an approach called RBF Neural Network Predictive Control. This method can be applied to various predictive control systems in engineering applications. The RBF (Radial Basis Function) neural network serves as a powerful computational tool for solving complex prediction and control problems. By implementing RBF networks with appropriate activation functions and center selection algorithms, we can achieve accurate prediction results and optimized control strategies. The typical implementation involves setting up network architecture with input normalization, hidden layer processing using Gaussian functions, and output layer calculations for control signal generation. Proper training methods like orthogonal least squares learning enhance the network's adaptation capabilities. Therefore, RBF neural network predictive control represents a highly promising methodology applicable across multiple domains including industrial automation, robotics, and process control systems.