RBF Neural Network Identification and Simulation

In my graduate thesis written in Chinese, I developed a comprehensive program for RBF neural network identification simulation, which I believe will be valuable for researchers in this field. The study begins with an explanation of RBF neural network fundamentals, including Gaussian activation functions and weight adjustment mechanisms, along with their applications in system identification and pattern recognition. The core implementation involves MATLAB-based architecture with three main components: a data preprocessing module that normalizes input signals, a network training module implementing gradient descent learning algorithm with adjustable learning rates, and a simulation engine that tests identification accuracy under various conditions. The program structure allows users to configure network parameters like hidden layer neurons and spread constants through a configuration file. Through systematic experiments, I validated the program's reliability by testing identification performance with different input patterns including step signals and sinusoidal waves. The simulation results demonstrate the network's capability to approximate nonlinear systems with mean squared error below 0.01 in optimal cases. The conclusion summarizes key findings about RBF network convergence properties and provides recommendations for future enhancements, such as incorporating adaptive learning rate algorithms and parallel computing techniques to improve computational efficiency. This work aims to contribute new methodologies for neural network-based system identification research.