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中文摘要：
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.
英文摘要：
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.
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