A generalized minimal residual based iterative back propagation algorithm for polynomial nonlinear models
In this paper, a back propagation algorithm is proposed for polynomial nonlinear models using generalized minimal residual method. This algorithm, based on Arnoldi’s method, can be regarded as a modified gradient descent iterative algorithm, and provides several advantages over the traditional gradi...
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| Vydané v: | Systems & control letters Ročník 153; s. 104966 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.07.2021
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| ISSN: | 0167-6911, 1872-7956 |
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| Abstract | In this paper, a back propagation algorithm is proposed for polynomial nonlinear models using generalized minimal residual method. This algorithm, based on Arnoldi’s method, can be regarded as a modified gradient descent iterative algorithm, and provides several advantages over the traditional gradient descent iterative algorithm: (1) has less computational efforts for systems with missing data/large-scale systems; (2) does not require the eigenvalue calculation in step-length design; (3) adaptively computes the step-length in each iteration. Therefore, it can be employed for large-scale system identification. The feasibility and effectiveness of the proposed algorithm are established in theory and demonstrated by two simulation examples. |
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| AbstractList | In this paper, a back propagation algorithm is proposed for polynomial nonlinear models using generalized minimal residual method. This algorithm, based on Arnoldi’s method, can be regarded as a modified gradient descent iterative algorithm, and provides several advantages over the traditional gradient descent iterative algorithm: (1) has less computational efforts for systems with missing data/large-scale systems; (2) does not require the eigenvalue calculation in step-length design; (3) adaptively computes the step-length in each iteration. Therefore, it can be employed for large-scale system identification. The feasibility and effectiveness of the proposed algorithm are established in theory and demonstrated by two simulation examples. |
| ArticleNumber | 104966 |
| Author | Rong, Yingjiao Zhong, Hongxiu Chandra, Budi Zhu, Quanmin Chen, Jing |
| Author_xml | – sequence: 1 givenname: Jing surname: Chen fullname: Chen, Jing email: chenjing1981929@126.com organization: School of Science, Jiangnan University, Wuxi 214122, PR China – sequence: 2 givenname: Yingjiao surname: Rong fullname: Rong, Yingjiao email: czx20100228@126.com organization: Science and Technology on Near-Surface Detection Laboratory, Wuxi 214028, PR China – sequence: 3 givenname: Quanmin surname: Zhu fullname: Zhu, Quanmin email: quan.zhu@uwe.ac.uk organization: Department of Engineering Design and Mathematics, University of the West of England, Bristol BS16 1QY, UK – sequence: 4 givenname: Budi surname: Chandra fullname: Chandra, Budi email: Budi.Chandra@uwe.ac.uk organization: Department of Engineering Design and Mathematics, University of the West of England, Bristol BS16 1QY, UK – sequence: 5 givenname: Hongxiu surname: Zhong fullname: Zhong, Hongxiu email: zhonghongxiu@126.com organization: School of Science, Jiangnan University, Wuxi 214122, PR China |
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| SubjectTerms | Arnoldi’s method Back propagation Convergence analysis Gradient descent iterative algorithm Nonlinear model |
| Title | A generalized minimal residual based iterative back propagation algorithm for polynomial nonlinear models |
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