Radial Basis Function Neural Network MATLAB Source Code
Application Background When BP networks are used for function approximation, the weight adjustment employs negative gradient descent method which has limitations such as slow convergence and local minima. Radial Basis Function (RBF) networks outperform BP networks in approximation capability, classification performance, and learning speed. MATLAB provides four radial basis function-related functions that create two-layer neural networks, with the first layer being radial basis layers and the second layer being either linear or competitive layers. The primary differences lie in their weight/threshold calculation methods and the presence of thresholds. Key Technology RBF networks can approximate arbitrary nonlinear functions, handle complex system patterns that are difficult to analyze analytically, demonstrate excellent generalization capability, and achieve rapid learning convergence speeds.