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题名:
Adaptive fading Kalman filter with an application
作者: Qijun Xia ; Ming Rao ; Yiqun Ying ; Xuemin Shen
关键词: Kalman filter ; state estimation ; adaptive estimation ; discrete system ; industrial processes
刊名: Automatica
发表日期: 1994
卷: 30, 期:8, 页:1333-1338
收录类别: 其他
合作性质: 其它
摘要: A new adaptive state estimation algorithm, namely adaptive fading Kalman filter (AFKF), is proposed to solve the divergence problem of Kalman filter. A criterion function is constructed to measure the optimality of Kalman filter. The forgetting factor in AFKF is adaptively adjusted by minimizing the defined criterion function using measured outputs. The algorithm remains convergent and tends to be optimal in the presence of model errors. It has been successfully applied to the headbox of a paper-making machine for state estimation.
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.iscas.ac.cn/handle/311060/1341
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Qijun Xia,Ming Rao,Yiqun Ying,et al. Adaptive fading Kalman filter with an application[J]. Automatica,1994-01-01,30(8):1333-1338.
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