MATLAB Simulation Code for RBF Neural Network Adaptive Control

MATLAB simulation code for RBF neural network adaptive control. This implementation demonstrates how RBF (Radial Basis Function) neural networks can achieve adaptive control through online learning and parameter adjustment. The code structure includes neural network initialization, weight adaptation algorithms, and real-time control signal generation. Key MATLAB functions employed in this simulation involve neural network training routines, system modeling, and performance evaluation metrics. The RBF network architecture typically consists of input layer, hidden layer with Gaussian activation functions, and output layer for control signal calculation. Through this simulation, users can analyze how the neural network adapts to system dynamics and maintains control performance under varying conditions. The implementation provides insights into gradient descent learning algorithms, Lyapunov stability analysis for adaptive systems, and real-time parameter tuning methodologies. This code serves as an educational tool for understanding both theoretical principles and practical implementation aspects of neural network-based adaptive control systems using MATLAB's simulation capabilities.