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Estimation and identification using ...
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Chen, Chii-Chuan.
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Estimation and identification using a real-time MME algorithm.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Estimation and identification using a real-time MME algorithm./
作者:
Chen, Chii-Chuan.
面頁冊數:
158 p.
附註:
Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 4001.
Contained By:
Dissertation Abstracts International64-08B.
標題:
Engineering, Mechanical. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3102350
Estimation and identification using a real-time MME algorithm.
Chen, Chii-Chuan.
Estimation and identification using a real-time MME algorithm.
- 158 p.
Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 4001.
Thesis (Ph.D.)--State University of New York at Buffalo, 2003.
A real-time or pseudo real-time (RTMME) estimation technique is presented in current research, based on the Minimum Model Error estimation algorithm. Given a set of measurements and with the nominal sensor variance information, the RTMME then can be performed by "windowing", "swapping", and "push-back" to find an optimal weighting for further use. The time needed for estimating the states are small enough that the RTMME could be used as a "real-time" estimator.Subjects--Topical Terms:
783786
Engineering, Mechanical.
Estimation and identification using a real-time MME algorithm.
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Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 4001.
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A real-time or pseudo real-time (RTMME) estimation technique is presented in current research, based on the Minimum Model Error estimation algorithm. Given a set of measurements and with the nominal sensor variance information, the RTMME then can be performed by "windowing", "swapping", and "push-back" to find an optimal weighting for further use. The time needed for estimating the states are small enough that the RTMME could be used as a "real-time" estimator.
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Next, system identification based on the RTMME is shown. This system identification may also be employed in pseudo real-time. A correlation technique is employed to select the best candidate function; followed by the linear and nonlinear least squares methods for the estimation of the model parameters/coefficients.
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Finally, an important application for RTMME is demonstrated. The RTMME is shown to serve as a sensor failure and/or system malfunction detection tool. These uses are demonstrated by examples.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3102350
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