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TISO-OEAR模型的分解递推最小二乘辨识方法 被引量:5

Decomposition-based Recursive Least Squares Algorithm for TISO-OEAR Model
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摘要 针对输出误差模型参数估计过程中的计算量较大的问题,提出了基于分解的两输入单输出(TISO)输出误差自回归模型(OEAR)的分解递推最小二乘(DRLS)算法.基本的思想是分解TISO系统为3个子系统,并通过递推最小二乘分别辨识每个子系统.DRLS算法是解决大规模系统的计算量大和复杂辨识模型的辨识难题的一种有效的方法.最后通过仿真实例验证和分析了所提出算法的有效性与优越性,并对两种算法的特点进行了总结. To address the problem of the large amount of computation required in the parameter estimation process of output error models,we propose a decomposition-based recursive least squares( DRLS) algorithm. The basic idea is to decompose a two-input single-output( TISO) system into three subsystems,and then identify each of the three subsystems. The DRLS algorithm is an effective method for solving large computing problems and the complex identification models of large-scale systems. We perform a simulation to verify the validity and superiority of the proposed algorithm,and summarize the characteristics of the proposed and conventional algorithms.
出处 《信息与控制》 CSCD 北大核心 2016年第3期294-300,共7页 Information and Control
基金 国家自然科学基金资助项目(61174021) 江苏省产学研联合创新资金前瞻性联合研究资助项目(BY2014023-31) 江苏省"六大人才高峰"资助项目(WLW-007)
关键词 分解技术 递推辨识 最小二乘 参数估计 两输入单输出 decomposition technique recursive identification least squares parameter estimation two-input single-output
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