An RBF Neural Network Implementation Using K-Means Clustering

In this document, the author presents an RBF neural network based on K-means clustering. While the code annotations provide clear explanations, some readers might benefit from additional technical context. K-means clustering is an unsupervised learning algorithm that partitions data samples into K clusters by iteratively computing distances between each sample and cluster centroids, assigning samples to their nearest centers. The algorithm typically initializes centroids randomly and refines them through multiple iterations until convergence. The RBF neural network employs radial basis functions as activation functions, commonly using Gaussian functions where the width parameter (sigma) controls the receptive field. Key implementation aspects include: using K-means to determine optimal RBF center positions, calculating Euclidean distances between input vectors and centers, and solving the output layer weights through linear regression or pseudoinverse methods. This architecture enables non-linear mapping of input data to higher-dimensional space where linear separation becomes feasible, making it effective for classification, regression, and clustering tasks. For code-specific implementation details such as centroid initialization strategies, distance computation optimizations, or weight calculation methods, please refer to the annotated code. Should you require further clarification on the algorithmic integration or mathematical foundations, I welcome inquiries via email and will provide detailed explanations.