Detailed Explanation of RBF Neural Network Algorithm with Code Examples

In this article, I will provide a detailed explanation of the RBF neural network algorithm. The content includes multiple practical examples accompanied by complete program files to facilitate easier comprehension. Through these examples and program files, you will gain a solid understanding of RBF neural network implementation specifics. The algorithm explanation covers key components such as radial basis function selection, hidden layer configuration using Gaussian activation functions, and weight optimization through least squares methods. Whether you are a beginner or have some experience in neural networks, this article will be beneficial for understanding RBF network architecture, including how to implement the forward propagation using matrix operations and how to train the network using center selection algorithms like k-means clustering for optimal hidden node placement. The provided code demonstrates practical implementation of basis function calculations and network training procedures with parameter tuning guidelines.