Maximum likelihood‐based gradient estimation for multivariable nonlinear systems using the multiinnovation identification theory
Summary This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is ado...
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| Veröffentlicht in: | International journal of robust and nonlinear control Jg. 30; H. 14; S. 5446 - 5463 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Bognor Regis
Wiley Subscription Services, Inc
25.09.2020
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| ISSN: | 1049-8923, 1099-1239 |
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| Abstract | Summary
This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is adopted to separate the parameters of the nonlinear block from the parameters of the linear block. By combining the model decomposition technique and the hierarchical identification principle, a key term separation‐based maximum likelihood recursive extended stochastic gradient algorithm with reduced computational complexity is presented to estimate all the parameters directly. By introducing the multiinnovation identification theory, a key term separation‐based maximum likelihood multiinnovation extended stochastic gradient algorithm is proposed to improve the parameter estimation accuracy. The simulation results illustrate the effectiveness of the proposed methods. |
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| AbstractList | This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is adopted to separate the parameters of the nonlinear block from the parameters of the linear block. By combining the model decomposition technique and the hierarchical identification principle, a key term separation‐based maximum likelihood recursive extended stochastic gradient algorithm with reduced computational complexity is presented to estimate all the parameters directly. By introducing the multiinnovation identification theory, a key term separation‐based maximum likelihood multiinnovation extended stochastic gradient algorithm is proposed to improve the parameter estimation accuracy. The simulation results illustrate the effectiveness of the proposed methods. Summary This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is adopted to separate the parameters of the nonlinear block from the parameters of the linear block. By combining the model decomposition technique and the hierarchical identification principle, a key term separation‐based maximum likelihood recursive extended stochastic gradient algorithm with reduced computational complexity is presented to estimate all the parameters directly. By introducing the multiinnovation identification theory, a key term separation‐based maximum likelihood multiinnovation extended stochastic gradient algorithm is proposed to improve the parameter estimation accuracy. The simulation results illustrate the effectiveness of the proposed methods. |
| Author | Xia, Huafeng Xu, Ling Ji, Yan Alsaedi, Ahmed Hayat, Tasawar |
| Author_xml | – sequence: 1 givenname: Huafeng orcidid: 0000-0003-2360-1856 surname: Xia fullname: Xia, Huafeng email: xiahuafeng1981@126.com organization: Taizhou University – sequence: 2 givenname: Yan orcidid: 0000-0002-3245-2437 surname: Ji fullname: Ji, Yan organization: Qingdao University of Science and Technology – sequence: 3 givenname: Ling orcidid: 0000-0002-5040-5634 surname: Xu fullname: Xu, Ling organization: Wuxi Vocational Institute of Commerce – sequence: 4 givenname: Ahmed surname: Alsaedi fullname: Alsaedi, Ahmed organization: King Abdulaziz University – sequence: 5 givenname: Tasawar surname: Hayat fullname: Hayat, Tasawar organization: King Abdulaziz University |
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| SubjectTerms | Algorithms Computer simulation Identification maximum likelihood Maximum likelihood estimation multiinnovation identification theory nonlinear system Nonlinear systems Parameter estimation Parameter identification Separation |
| Title | Maximum likelihood‐based gradient estimation for multivariable nonlinear systems using the multiinnovation identification theory |
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