Cluster-Based RBF Neural Network Algorithm

The article introduces a cluster-based RBF neural network algorithm designed for function approximation of input-output data, facilitating both prediction and classification tasks. The algorithm's core methodology involves partitioning input data into distinct clusters using unsupervised clustering techniques (such as K-means or Gaussian mixture models), followed by applying RBF neural networks within each cluster for localized learning and optimization. This approach overcomes limitations of traditional neural networks in handling high-dimensional data while enhancing model accuracy and stability through distributed representation learning. Implementation typically involves: 1) Cluster center initialization using Euclidean distance metrics, 2) Gaussian kernel function computation for hidden layer activation, and 3) Linear output layer training via least-squares optimization. Notably, this algorithm demonstrates significant potential in big data processing and machine learning applications, particularly for scenarios requiring adaptive resolution and nonlinear pattern recognition.