| Hammerstein OEMA系统的辅助模型最小二乘辨识 |
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| 引用本文： | 初燕云,王冬青,杨国为. Hammerstein OEMA系统的辅助模型最小二乘辨识[J]. 科学技术与工程, 2009, 9(22) |
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| 作者姓名： | 初燕云 王冬青 杨国为 |
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| 作者单位： | 青岛大学自动化工程学院,青岛,266071 |
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| 基金项目： | 山东省高等学校优秀青年教师国内访问学者项目、国家自然科学基金 |
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| 摘 要： | 针对Hammerstein 输出误差自回归(OEMA)模型, 将关键变量分离原理与辅助模型辨识思想相结合,提出了基于关键变量分离的辅助模型递推增广最小二乘辨识方法.该方法能获得系统参数估计和噪声参数估计,且能实现在线辨识.
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| 关 键 词： | Hammerstein模型 关键变量分离原理 辅助模型 递推辨识 |
| 收稿时间： | 2009-08-10 |
| 修稿时间： | 2009-08-10 |
Auxiliary model based least squares identification for Hammerstein OEMA systems |
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| Chu Yanyun,Wang Dongqing and Yang Guowei. Auxiliary model based least squares identification for Hammerstein OEMA systems[J]. Science Technology and Engineering, 2009, 9(22) |
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| Authors: | Chu Yanyun Wang Dongqing Yang Guowei |
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| Affiliation: | College of Automation Engineering, Qingdao University,College of Automation Engineering, Qingdao University |
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| Abstract: | This paper combines the key-term separation principle and the auxiliary model identification idea, and presents the auxiliary model based recursive extended least squares algorithms by using the key-term separation principle for Hammerstein output error autoregression (OEMA) systems. The proposed algorithms can obtain the system model parameter estimates and the noise model parameter estimates, and can be implemented on-line. |
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| Keywords: | Hammerstein models key-term separation principle auxiliary models recursive identification |
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