计算机应用 ›› 2015, Vol. 35 ›› Issue (5): 1367-1372.DOI: 10.11772/j.issn.1001-9081.2015.05.1367

• 人工智能 • 上一篇    下一篇

基于反馈教学优化算法的混沌系统参数辨识

李瑞国1, 张宏立1, 王雅2   

  1. 1. 新疆大学 电气工程学院, 乌鲁木齐 830047;
    2. 新疆大学 机械工程学院, 乌鲁木齐 830047
  • 收稿日期:2014-12-08 修回日期:2015-01-02 出版日期:2015-05-10 发布日期:2015-05-14
  • 通讯作者: 李瑞国
  • 作者简介:李瑞国(1986-),男(满族),河北秦皇岛人,硕士研究生,主要研究方向:混沌理论、智能优化、模式识别; 张宏立(1972-),男,湖南长沙人,副教授,博士,主要研究方向:混沌理论、智能算法、系统辨识; 王雅(1990-),女,河北邢台人,硕士研究生,主要研究方向:多目标智能算法应用、故障诊断与信号处理.
  • 基金资助:

    国家自然科学基金资助项目(61463047).

Parameter identification in chaotic system based on feedback teaching-learning-based optimization algorithm

LI Ruiguo1, ZHANG Hongli1, WANG Ya2   

  1. 1. School of Electrical Engineering, Xinjiang University, Urumqi Xinjiang 830047, China;
    2. School of Mechanical Engineering, Xinjiang University, Urumqi Xinjiang 830047, China
  • Received:2014-12-08 Revised:2015-01-02 Online:2015-05-10 Published:2015-05-14

摘要:

针对传统智能优化算法对混沌系统参数辨识精度低、速度慢的问题,提出一种基于反馈教学优化算法的混沌系统参数辨识的新方法.该方法以教学优化算法为基础,在教授-学习阶段之后加入反馈阶段,同时将参数辨识问题转化为参数空间上的函数优化问题.分别以三维二次自治广义Lorenz系统、Jerk系统和Sprott-J系统为待辨识模型,对粒子群优化算法、量子粒子群优化算法、教学优化算法及反馈教学优化算法进行了对比实验,反馈教学优化算法辨识误差为零,搜索次数明显减少.仿真结果表明,反馈教学优化算法明显提高了混沌系统参数辨识精度和速度,验证了该算法的可行性和有效性.

关键词: 教授阶段, 学习阶段, 反馈阶段, 混沌系统, 参数辨识

Abstract:

Concerning low precision and slow speed of traditional intelligent optimization algorithm for parameter identification in chaotic system, a new method of parameter identification in chaotic system based on feedback teaching-learning-based optimization algorithm was proposed. This method was based on the teaching-learning-based optimization algorithm, where the feedback stage was introduced at the end of the teaching and learning stage. At the same time the parameter identification problem was converted into a function optimization problem in parameter space. Three-dimensional quadratic autonomous generalized Lorenz system, Jerk system and Sprott-J system were taken as models respectively, intercomparison experiments among particle swarm optimization algorithm, quantum particle swarm optimization algorithm, teaching-learning-based optimization algorithm and feedback teaching-learning-based optimization algorithm were conducted. The identification error of the feedback teaching-learning-based optimization algorithm was zero, meanwhile, the search times was decreased significantly. The simulation results show that the feedback teaching-learning-based optimization algorithm improves the precision and speed of the parameter identification in chaotic system markedly, so the feasibility and effectiveness of the algorithm are well demonstrated.

Key words: teaching stage, learning stage, feedback stage, chaotic system, parameter identification

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