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International Journal of Systems Science Volume 50, 2019 - Issue 14
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Articles

Maximum likelihood based identification methods for rational models

Jing Chena School of Science, Jiangnan University, Wuxi, People's Republic of ChinaCorrespondence[email protected]
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Feng Dingb College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, People's Republic of China;c School of Internet of Things Engineering, Jiangnan University, Wuxi, People's Republic of ChinaORCID Iconhttps://orcid.org/0000-0002-2721-2025View further author information
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Quanmin Zhud Department of Engineering Design and Mathematics, University of the West of England, Bristol, UKView further author information
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Yanjun Liuc School of Internet of Things Engineering, Jiangnan University, Wuxi, People's Republic of ChinaView further author information
Pages 2579-2591 | Received 21 Sep 2017, Accepted 18 Sep 2019, Published online: 01 Oct 2019

Citations (5)

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S. X. Jing, R. R. Liu & L. Chen. (2022) Identification of a nonlinear rational model based on bias compensated multi-innovation stochastic gradient algorithm. Automatika 63:4, pages 785-792.
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Articles from other publishers (4)

Shaoxue Jing, Tianhong Pan & Quanmin Zhu. (2022) Identification of an ARX-type Nonlinear Rational Model Based on the Renyi Error Entropy and the Epanechnikov Kernel. International Journal of Control, Automation and Systems 20:10, pages 3233-3240.
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Shaoxue Jing, Janice Kiely & Richard Luxton. (2022) Bias compensated stochastic gradient algorithm for identification of an ARX‐type nonlinear rational model and its application in modeling of the dynamic of the cellular toxicity. International Journal of Robust and Nonlinear Control 32:9, pages 5268-5280.
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Bi-Peng Chen, Yun Chen, Guo-Qiang Zeng & Qingshan She. (2022) Fractional-order convolutional neural networks with population extremal optimization. Neurocomputing 477, pages 36-45.
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Yuejiang Ji & Lixin Lv. (2021) M-Decomposed Least Squares and Recursive Least Squares Identification Algorithms for Large-Scale Systems. IEEE Access 9, pages 139466-139472.
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