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基于鲁棒优化的系统辨识算法研究

钱富才 黄姣茹 秦新强

钱富才, 黄姣茹, 秦新强. 基于鲁棒优化的系统辨识算法研究. 自动化学报, 2014, 40(5): 988-993. doi: 10.3724/SP.J.1004.2014.00988
引用本文: 钱富才, 黄姣茹, 秦新强. 基于鲁棒优化的系统辨识算法研究. 自动化学报, 2014, 40(5): 988-993. doi: 10.3724/SP.J.1004.2014.00988
QIAN Fu-Cai, HUANG Jiao-Ru, QIN Xin-Qiang. Research on Algorithm for SystemIdentification Based on RobustOptimization. ACTA AUTOMATICA SINICA, 2014, 40(5): 988-993. doi: 10.3724/SP.J.1004.2014.00988
Citation: QIAN Fu-Cai, HUANG Jiao-Ru, QIN Xin-Qiang. Research on Algorithm for SystemIdentification Based on RobustOptimization. ACTA AUTOMATICA SINICA, 2014, 40(5): 988-993. doi: 10.3724/SP.J.1004.2014.00988

基于鲁棒优化的系统辨识算法研究

doi: 10.3724/SP.J.1004.2014.00988
基金项目: 

国家自然科学基金(61273127),高等学校博士学科点专项科研基金(20116118110008)资助

详细信息
    作者简介:

    黄姣茹 西安理工大学自动化与信息工程学院博士研究生. 主要研究方向为鲁棒优化,系统辨识和最优控制. E-mail:huangjiaoru@126.com

Research on Algorithm for SystemIdentification Based on RobustOptimization

Funds: 

Supported by National Natural Science Foundation of China (61273127), and Specialized Research Fund for the Doctoral Program of Higher Education (20116118110008)

  • 摘要: 输入-输出数据是解决系统辨识问题的关键要素,传统的辨识理论除了假定影响输入-输出数据干扰的密度函数已知外,还要假定输入-输出数据能够精确获得,完全忽略了所用数据的质量.本文突破了传统理论的两个假设,首先用工程上易于获得的干扰的有界集合代替干扰的密度函数,并在特定数据不确定性结构下,考虑了数据质量问题,然后,以半定规划为基础,导出了鲁棒对等式,从而将系统辨识转化为对数据质量具有鲁棒性的优化问题,通过求解该优化问题,得到了一种新的鲁棒优化辨识方法,仿真结果表明了新方法的可行性和有效性.
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出版历程
  • 收稿日期:  2013-04-08
  • 修回日期:  2013-08-12
  • 刊出日期:  2014-05-20

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