Explore MATLAB source code curated for "径向基函数神经网络" with clean implementations, documentation, and examples.
Efficiently construct radial basis function neural networks using training datasets and visualize model performance through input-output grid plots with implementation insights.
This is a radial basis function neural network implementation that approximates a two-dimensional function using RBF network learning algorithms, with weight adjustments performed through the LMS algorithm. The implementation includes Gaussian activation functions in hidden layers and linear output combinations.
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.
Training a radial basis function neural network using sample data and visualizing its input-output mapping through grid plots with implementation details