物理学报Issue(19)：106-111,6.DOI:10.7498/aps.62.190508
基于量子粒子群算法的混沌系统参数辨识
Parameter identification in chaotic systems by means of quantum particle swarm optimization
张宏立 1宋莉莉1
作者信息
- 1. 新疆大学电气工程学院，乌鲁木齐 830047
- 折叠
摘要
Abstract
Aiming at the parameter identification problem in chaotic systems, we propose the quantum particle swarm optimization algorithm based on the swarm intelligence particle swarm optimization. The test functions show that the method has good global optimization. Then the method is applied to the parameter identification problem of the chaotic system. We transform the parameter identification problem into the optimization in the multi-dimensional function space. Through research on the balance board thermal convection in a typical chaotic Lorenz system, the proposed method has been compared with the basic algorithm and the genetic algorithm. Simulation results show that the proposed algorithm is effective, and is very important to the development of chaos theory.关键词
量子粒子群算法/混沌系统/系统辨识Key words
quantum particle swarm optimization/chaotic system/system identification引用本文复制引用
张宏立,宋莉莉..基于量子粒子群算法的混沌系统参数辨识[J].物理学报,2013,(19):106-111,6.
