MATLAB Radial Basis Function Neural Network Model Computation Program Source Code

This article presents a comprehensive MATLAB implementation of a Radial Basis Function Neural Network (RBFNN) model computation program. The RBFNN model is particularly effective for solving various machine learning problems including classification tasks, regression analysis, and clustering applications. The implementation involves key components such as proper network parameter initialization, where weights and biases are typically set using Gaussian distribution or Xavier initialization methods. The training process employs optimization algorithms like gradient descent or specialized RBF training techniques, with adjustable learning rates and convergence criteria. The source code architecture allows for customization through various activation functions (commonly Gaussian functions) and loss functions (Mean Squared Error for regression, Cross-Entropy for classification). Users can modify network parameters including the number of hidden neurons, spread constants for radial basis functions, and training epochs to optimize model performance. The modular design enables easy extension for different application scenarios by adjusting the network structure parameters in the source code. Key functions implemented include data normalization routines, center selection algorithms (K-means or random selection), and weight update mechanisms using backpropagation or direct calculation methods. The code follows MATLAB best practices with clear commenting and structure, making it suitable for both educational purposes and practical implementations in research and industrial applications.