Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory
This paper considers the parameter and order estimation for multiple-input single-output nonlinear systems. Since the orders of the system are unknown, a high-dimensional identification model and a sparse parameter vector are established to include all the valid inputs and basic parameters. Applying...
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| Published in | Journal of the Franklin Institute Vol. 359; no. 5; pp. 2317 - 2339 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elmsford
Elsevier Ltd
01.03.2022
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper considers the parameter and order estimation for multiple-input single-output nonlinear systems. Since the orders of the system are unknown, a high-dimensional identification model and a sparse parameter vector are established to include all the valid inputs and basic parameters. Applying the data filtering technique, the input-output data are filtered and the original identification model with autoregressive noise is changed into the identification model with white noise. Based on the compressed sensing recovery theory, a data filtering-based orthogonal matching pursuit algorithm is presented for estimating the system parameters and the orders. The presented method can obtain highly accurate estimates from a small number of measurements by finding the highest absolute inner product. The simulation results confirm that the proposed algorithm is effective for recovering the model of the multiple-input single-output Hammerstein finite impulse response systems. |
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| AbstractList | This paper considers the parameter and order estimation for multiple-input single-output nonlinear systems. Since the orders of the system are unknown, a high-dimensional identification model and a sparse parameter vector are established to include all the valid inputs and basic parameters. Applying the data filtering technique, the input-output data are filtered and the original identification model with autoregressive noise is changed into the identification model with white noise. Based on the compressed sensing recovery theory, a data filtering-based orthogonal matching pursuit algorithm is presented for estimating the system parameters and the orders. The presented method can obtain highly accurate estimates from a small number of measurements by finding the highest absolute inner product. The simulation results confirm that the proposed algorithm is effective for recovering the model of the multiple-input single-output Hammerstein finite impulse response systems. |
| Author | Zhang, Xiao Xu, Ling Kang, Zhen Ji, Yan |
| Author_xml | – sequence: 1 givenname: Yan orcidid: 0000-0002-3245-2437 surname: Ji fullname: Ji, Yan email: yjichina@163.com, yjichinaqd@qust.edu.cn organization: College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, PR China – sequence: 2 givenname: Zhen surname: Kang fullname: Kang, Zhen organization: College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, PR China – sequence: 3 givenname: Xiao surname: Zhang fullname: Zhang, Xiao email: xzhang13@126.com organization: School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China – sequence: 4 givenname: Ling surname: Xu fullname: Xu, Ling email: lingxu0848@163.com organization: School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China |
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| SubjectTerms | Algorithms Autoregressive models Data recovery Estimation Filtration Impulse response Input output analysis Matched pursuit MISO (control systems) Nonlinear systems Parameter identification Regression analysis White noise |
| Title | Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory |
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