Missing Output Identification Model Based Recursive Least Squares Algorithm for a Distributed Parameter System
This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least...
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| Published in | International journal of control, automation, and systems Vol. 16; no. 1; pp. 150 - 157 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Bucheon / Seoul
Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
01.02.2018
Springer Nature B.V 제어·로봇·시스템학회 |
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| Online Access | Get full text |
| ISSN | 1598-6446 2005-4092 |
| DOI | 10.1007/s12555-016-0606-5 |
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| Abstract | This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least squares algorithm is derived to estimate the unknown parameters of the lumped parameter system. Furthermore, the parameters of the distributed parameter system can be computed by the estimated parameters of the lumped parameter system. The simulation results indicate that the proposed method is effective. |
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| AbstractList | This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least squares algorithm is derived to estimate the unknown parameters of the lumped parameter system. Furthermore, the parameters of the distributed parameter system can be computed by the estimated parameters of the lumped parameter system. The simulation results indicate that the proposed method is effective. This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least squares algorithm is derived to estimate the unknown parameters of the lumped parameter system. Furthermore, the parameters of the distributed parameter system can be computed by the estimated parameters of the lumped parameter system. The simulation results indicate that the proposed method is effective. KCI Citation Count: 16 |
| Author | Jiang, Bin Li, Juan Chen, Jing |
| Author_xml | – sequence: 1 givenname: Jing surname: Chen fullname: Chen, Jing organization: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics – sequence: 2 givenname: Bin surname: Jiang fullname: Jiang, Bin email: binjiang@nuaa.edu.cn organization: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics – sequence: 3 givenname: Juan surname: Li fullname: Li, Juan organization: College of Mechanical and Electrical Engineering, Qingdao Agricultural University |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002311226$$DAccess content in National Research Foundation of Korea (NRF) |
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| Keywords | parameter estimation missing output identification model recursive least squares Distributed parameter system finite difference method |
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| Title | Missing Output Identification Model Based Recursive Least Squares Algorithm for a Distributed Parameter System |
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