The Vector Clustering Based on the Recursive Particle Swarm Optimization with Radial Basis Function Networks Modeling System

The vector clustering plays an important role to the applications of information science, such as information compression. The particle swarm optimization is used to implement the clustering operation. In this paper, a Recursive Particle Swarm Optimization (RPSO) is proposed to solve dynamic optimization problems where the data is obtained not once but one by one. The position of each particle swarm is updated recursively based on the continuous data and the historical knowledge. The experiment results indicate that RPSO-based radial basis function networks needs fewer radial basis functions and gives more accurate results than traditional PSO in solving dynamic problems. Then the proposed RPSO is suggested to cluster statistic counting vectors in order to enhance the performance of the data compression.