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| TISO-OEAR模型的分解递推最小二乘辨识方法 | |
| 引用本文： | 石文林,卢先领.TISO-OEAR模型的分解递推最小二乘辨识方法[J].信息与控制,2016,45(3):294-300. |
| 作者姓名： | 石文林 卢先领 |
| 作者单位： | 1. 江南大学轻工过程先进控制国家教育部重点实验室, 江苏 无锡 214122; 2. 江南大学物联网工程学院, 江苏 无锡 214122 |
| 基金项目： | 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） |
| 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． |
| 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 |
| 收稿时间： | 2015-05-01 |
Decomposition-based Recursive Least Squares Algorithm for TISO-OEAR Model |
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| SHI Wenlin,LU Xianling.Decomposition-based Recursive Least Squares Algorithm for TISO-OEAR Model[J].Information and Control,2016,45(3):294-300. | |
| Authors: | SHI Wenlin LU Xianling |
| Affiliation: | 1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China; 2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China |
| Abstract: | 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. |
| Keywords: | decomposition technique recursive identification least squares parameter estimation two-input single-output |
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