NDLI: Model reduction for identification of ARX models

Content Provider	IEEE Xplore Digital Library
Author	Jianhong Wang Zhu Yong-hong
Copyright Year	2012
Description	Author affiliation: School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, 333403, China (Jianhong Wang; Zhu Yong-hong)
Abstract	In this paper, we discuss the problem of model reduction in ARX system from the point of system identification. When consider the process model represented by the linear regression form, based on the asymptotic analysis results of the unknown parameters vector in the probability frame system, we derive the asymptotic variance matrix form of the unknown parameters vector in ARX system. When obtain the identified parameters vector, we apply the most popular model reduction method L2 method and derive the identification strategy about the unknown parameters vector in the reduced model. Furthermore, we analyse the asymptotic variance matrix form of the unknown parameters vector in the reduced model. Finally, the efficiency and possibility of the proposed strategy can be confirmed by the simulation example results.
Starting Page	2093
Ending Page	2098
File Size	529841
Page Count	6
File Format	PDF
ISBN	9781457720734
e-ISBN	9781457720741
DOI	10.1109/CCDC.2012.6244337
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models ARX system Estimation model reduction Educational institutions asymptotic variance analysis Reduced order systems Vectors Electronic mail System identification
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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