Recursive identification for multi-input-multi-output Hammerstein-Wiener system
In this paper, an identification method based on the recursive auxiliary variable least squares algorithm is proposed for a multi-input-multi-output Hammerstein-Wiener system with process noise. In the proposed identification method, the system is converted into the multivariate regression form unde...
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| Vydané v: | International journal of control Ročník 92; číslo 6; s. 1457 - 1469 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Taylor & Francis
03.06.2019
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| Abstract | In this paper, an identification method based on the recursive auxiliary variable least squares algorithm is proposed for a multi-input-multi-output Hammerstein-Wiener system with process noise. In the proposed identification method, the system is converted into the multivariate regression form under the condition that the nonlinear block in the output part is invertible. Then, the auxiliary variable is constructed, the parameters of the regression equations are identified, and the system parameter matrices can be obtained by matrix composition of the parameter product matrix. A theoretical analysis showed that the proposed method has uniform convergence when the process noise is white and has a finite variance. The effectiveness of the proposed method is validated through the experiments. |
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| AbstractList | In this paper, an identification method based on the recursive auxiliary variable least squares algorithm is proposed for a multi-input-multi-output Hammerstein-Wiener system with process noise. In the proposed identification method, the system is converted into the multivariate regression form under the condition that the nonlinear block in the output part is invertible. Then, the auxiliary variable is constructed, the parameters of the regression equations are identified, and the system parameter matrices can be obtained by matrix composition of the parameter product matrix. A theoretical analysis showed that the proposed method has uniform convergence when the process noise is white and has a finite variance. The effectiveness of the proposed method is validated through the experiments. |
| Author | Mao, Zhizhong Bai, Jing Pu, Tiecheng |
| Author_xml | – sequence: 1 givenname: Jing surname: Bai fullname: Bai, Jing organization: College of Electrical and Information Engineering, Beihua University – sequence: 2 givenname: Zhizhong surname: Mao fullname: Mao, Zhizhong email: maozhizhong@ise.neu.edu.cn organization: College of Information Science and Engineering, Northeastern University – sequence: 3 givenname: Tiecheng surname: Pu fullname: Pu, Tiecheng organization: College of Electrical and Information Engineering, Beihua University |
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| Title | Recursive identification for multi-input-multi-output Hammerstein-Wiener system |
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