Seven RBF Neural Network Code Implementations

In this article, I present seven source code implementations of RBF neural networks that I have collected, modified, and systematically organized. These implementations demonstrate various approaches to RBF network design, including different center selection methods (such as k-means clustering and random selection), diverse basis function configurations (Gaussian functions with varying width parameters), and multiple training algorithms (including gradient descent and least squares optimization). I am sharing these programs to facilitate knowledge exchange and welcome technical discussions about their implementation details. Beyond the source code, I can provide additional technical documentation, algorithm explanations, and implementation insights. The code includes key functions for network initialization, parameter optimization, and performance evaluation. If you are interested in RBF neural networks or want to understand the working principles behind these implementations - such as how the hidden layer centers are determined or how the output weights are calculated - please feel free to ask questions. I am eager to share my knowledge and practical experience in neural network implementation and optimization techniques.