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Hammerstein OEMA系统的辅助模型最小二乘辨识 预览 被引量:1

Auxiliary Model Based Least Squares Identification for Hammerstein OEMA Systems
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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。 The key-term separation principle and the auxiliary model identification idea,and presents the auxiliary model based recursive extended least squares algorithms for Hammerstein output error autoregression(OEMA) systems are combined.The proposed algorithms can obtain the system model parameter estimates and the noise model parameter estimates,and can be implemented on-line.
作者 初燕云 王冬青 杨国为 CHU Yan-yun, WANG Dong-qing, YANG Guo-wei ( College of Automation Engineering, Qingdao University, Qingdao 266071, P. R. China)
出处 《科学技术与工程》 2009年第22期 6837-6839,共3页 Science Technology and Engineering
基金 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(60673101)资助
关键词 HAMMERSTEIN模型 关键变量分离原理 辅助模型 递推辨识 Hammerstein models key-term separation principle auxiliary models recursive identification
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参考文献5

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二级参考文献15

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