基于递推粒子群优化算法的径向基函数网络模拟系统

Radial Basis Function Networks Modeling System Based on Recursive Particle Swarm Optimization Applications

提出一种用于解决递推估计问题的优化算法,该算法基于递推粒子群优化.递推估计问题获得的数据并非一次性获得,而是陆续获得.在递推的粒子群算法中,粒子基于过去的数据信息和新获取的数据递推地更新自己位置.实验结果表明,基于递推算法的径向基函数网络模拟系统只需要较少的径向基函数,同时在解决动态问题时能够比传统粒子群算法获得更准确的结果.

A Recursive Particle Swarm Optimization （R-PSO）is proposed to solve dynamic optimization problems where the data is ob-tained not once but one by one.In R-PSO,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 func-tions and meanwhile gives more accurate results than traditional PSO in solving dynamic problems.