摘要
针对传统辨识方法辨识非线性系统时存在的辨识精度低、收敛速度慢等问题,引入了一种基于混合引力搜索算法的非线性系统辨识方法。该混合优化算法是将粒子群算法中群体历史最优位置及自身历史最优位置的概念引入到引力搜索算法中,通过帮助粒子接近最优位置,改进了搜索算法中粒子的全局搜索能力,使得该混合算法的开采能力和探索能力得到更好的增强和平衡。对Wiener模型进行辨识,比较分析仿真结果,发现混合优化算法能够提高辨识精度并获得良好的辨识效果,验证了该算法的有效性和可行性。
In terms of the low identification accuracy and slow convergence speed of the traditional identification methods when the system is nonlinear,this paper introduces the global best and historical best of the particle swarm optimization into the gravitational search algorithm,which helps to approach the optimal position and improves the global searching ability of particles in GSA.The exploration and exploitation abilities of the hybrid algorithm can be enhanced and well-balanced.According to parameters identification of the Wiener model,the simulation results show that the GSAPSO algorithm can improve the identification accuracy and get good recognition results.The effectiveness and feasibility of the GSAPSO algorithm are verified.
作者
李欣欣
张宏立
LI Xinxin ZHANG Hongli(College of Electrical Engineering,Xinjiang University, Urumqi 830047, Chin)
出处
《青岛科技大学学报(自然科学版)》
CAS
2016年第5期562-566,共5页
Journal of Qingdao University of Science and Technology:Natural Science Edition
关键词
引力搜索算法
混合优化算法
全局搜索能力
非线性系统
gravitational search algorithm
hybrid algorithm
global searching ability
nonlinear system