Combined state and multi-innovation parameter estimation for an input non-linear state-space system using the key term separation
In this study, the authors study the state and parameter estimation problem for an input non-linear system consisting of a static non-linear block and a linear time-invariant state space subsystem. Using the filtering technique, a filtering based multi-innovation generalised stochastic gradient (SG)...
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| Published in: | IET control theory & applications Vol. 10; no. 13; pp. 1503 - 1512 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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The Institution of Engineering and Technology
29.08.2016
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| ISSN: | 1751-8644, 1751-8652 |
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| Abstract | In this study, the authors study the state and parameter estimation problem for an input non-linear system consisting of a static non-linear block and a linear time-invariant state space subsystem. Using the filtering technique, a filtering based multi-innovation generalised stochastic gradient (SG) algorithm is proposed for avoiding estimating the redundant parameters based on the key term separation technique. Compared with the multi-innovation generalised SG algorithm, the proposed algorithm has higher parameter estimation accuracy. Two simulation examples are provided to show that the proposed algorithm works well. |
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| AbstractList | In this study, the authors study the state and parameter estimation problem for an input non‐linear system consisting of a static non‐linear block and a linear time‐invariant state space subsystem. Using the filtering technique, a filtering based multi‐innovation generalised stochastic gradient (SG) algorithm is proposed for avoiding estimating the redundant parameters based on the key term separation technique. Compared with the multi‐innovation generalised SG algorithm, the proposed algorithm has higher parameter estimation accuracy. Two simulation examples are provided to show that the proposed algorithm works well. |
| Author | Ding, Feng Wang, Xuehai Hayat, Tasawar Alsaedi, Ahmed |
| Author_xml | – sequence: 1 givenname: Xuehai surname: Wang fullname: Wang, Xuehai organization: 2School of Mathematics and Physics, Henan University of Urban Construction, Pingdingshan 467036, People's Republic of China – sequence: 2 givenname: Feng surname: Ding fullname: Ding, Feng email: fding@jiangnan.edu.cn organization: 3Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia – sequence: 3 givenname: Tasawar surname: Hayat fullname: Hayat, Tasawar organization: 4Department of Mathematics, Quaid-I-Azam University, Islamabad 44000, Pakistan – sequence: 4 givenname: Ahmed surname: Alsaedi fullname: Alsaedi, Ahmed organization: 3Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
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| Keywords | key term separation linear systems state estimation problem nonlinear state-space system multiinnovation parameter estimation problem nonlinear systems redundant parameters multiinnovation generalised SG algorithm parameter estimation filtering based multiinnovation generalised stochastic gradient algorithm linear time-invariant state space subsystem gradient methods state-space methods input nonlinear system |
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| SubjectTerms | Algorithms Computer simulation Dynamical systems Filtering filtering based multiinnovation generalised stochastic gradient algorithm Filtration gradient methods input nonlinear system key term separation linear systems linear time‐invariant state space subsystem multiinnovation generalised SG algorithm multiinnovation parameter estimation problem nonlinear state‐space system nonlinear systems Nonlinearity Parameter estimation redundant parameters Regular Papers Separation state estimation problem state‐space methods |
| Title | Combined state and multi-innovation parameter estimation for an input non-linear state-space system using the key term separation |
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