RBF Neural Network Simulation (Primarily for Function Fitting and Pattern Classification)

RBF neural network simulation represents a widely-used algorithm for function fitting and pattern classification tasks. The implementation can be demonstrated through MATLAB example programs that typically utilize the newrb or newrbe functions to create radial basis function networks. This algorithm constructs neural networks with radial basis functions that effectively approximate complex nonlinear relationships and perform classification through Gaussian activation functions centered on prototype vectors. In practical applications, RBF neural networks are extensively employed in pattern recognition, signal processing, and prediction systems. Through appropriate example programs involving parameter optimization like spread constants and hidden layer sizing, we can better understand and apply this algorithm. By leveraging MATLAB's built-in demonstration scripts and toolbox functions, developers can deeply study the underlying principles including centroid selection methods and weight calculation techniques, thereby mastering both the theoretical foundations and practical applications of RBF neural networks.