RBF Network Prediction Using Clustering Algorithm in MATLAB

In this paper, we present an implementation of RBF network prediction using clustering algorithms in MATLAB. This methodology employs clustering algorithms to partition data into meaningful groups, followed by RBF network utilization for predictive modeling. The approach facilitates enhanced data understanding and analysis while delivering accurate prediction outcomes. The implementation leverages MATLAB's built-in functions for clustering (such as k-means via kmeans function) and RBF network construction (using newrb or newrbe functions) to create an integrated prediction system. Key implementation steps include data preprocessing, cluster center determination through clustering algorithms, RBF network initialization with obtained centers, network training using radial basis functions, and final prediction generation. Experimental validation demonstrates that combining clustering algorithms with RBF networks significantly improves prediction accuracy and provides more detailed, comprehensive analytical results. The MATLAB implementation allows for efficient parameter tuning, including cluster number optimization and spread factor adjustment for radial basis functions, ensuring robust performance across various datasets.