华南理工大学学报(自然科学版) ›› 2015, Vol. 43 ›› Issue (5): 73-77,113.doi: 10.3969/j.issn.1000-565X.2015.05.012

• 电子、通信与自动控制 • 上一篇    下一篇

基于确定学习的有阻尼受迫 Sine- Gordon 方程的辨识

董训德1,2 王聪1†   

  1. 1. 华南理工大学 自动化科学与工程学院,广东 广州 510640; 2. 华南理工大学 数学学院,广东 广州 510640
  • 收稿日期:2014-08-28 修回日期:2015-01-06 出版日期:2015-05-25 发布日期:2015-05-07
  • 通信作者: 王聪(1968-),男,博士,教授,主要从事智能控制、动态模式识别研究. E-mail:wangcong@scut.edu.cn
  • 作者简介:董训德(1985-),男,博士生,主要从事系统辨识及确定学习研究. E-mail: dong. xd@ mail. scut. edu. cn
  • 基金资助:

    国家杰出青年科学基金资助项目(61225014)

Identification of Damped and Driven Sine-Gordon Equation Based on Deterministic Learning

Dong Xun-de1,2 Wang Cong1   

  1. 1. School of Automation Science and Technology,South China University of Technology,Guangzhou 510640,Guangdong,China;2. School of Mathematics,South China Unirersity of Technology,Guangzhou 510640,Guangdong,China
  • Received:2014-08-28 Revised:2015-01-06 Online:2015-05-25 Published:2015-05-07
  • Contact: 王聪(1968-),男,博士,教授,主要从事智能控制、动态模式识别研究. E-mail:wangcong@scut.edu.cn
  • About author:董训德(1985-),男,博士生,主要从事系统辨识及确定学习研究. E-mail: dong. xd@ mail. scut. edu. cn
  • Supported by:
    Supported by the National Science Fundation for Distinguished Young Scholars of China(61225014)

摘要: 文中就一类有阻尼受迫 Sine-Gordon 方程的系统动态进行辨识研究. 首先利用有限差分理论,将由偏微分方程描述的无穷维 Sine-Gordon 方程近似为由一组常微分方程描述的有限维系统,然后证明该近似系统解的存在唯一性和收敛性,最后利用确定学习对该近似系统的系统动态进行辨识. 实验结果表明,文中方法可实现该类 Sine-Gordon 方程系统动态的局部准确辨识.

关键词: Sine-Gordon 方程, 系统辨识, 确定学习, 动力学

Abstract: Discussed in this paper is the identification of dynamics of a class of damped and driven Sine-Gordon (SG) equation. Firstly,SG equation described by partial differential equation (PDE),which is infinite dimen-sional,is approximated by a set of ordinary differential equation with finite dimension by means of finite difference method. Then,the existence,uniqueness and convergence of the solution of the approximated system are proofed.Finally,the dynamics of the approximated system is identified on the basis of deterministic learning. Experimental results show that the proposed method helps achieve locally accurate identification of SG equation dynamics.

Key words: Sine-Gordon equation, system identification, deterministic learning, dynamics