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Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator

Yang Shi Orcid Logo, Wenhan Zhao, Shuai Li Orcid Logo, Bin Li, Xiaobing Sun

Applied Soft Computing, Volume: 133, Start page: 109861

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.asoc.2022.109861

Abstract

The improvement of recurrent neural network (RNN) algorithms is one of target of many researchers, and these algorithms are wieldy used to solve time-variant problems in a variety of domains. A novel direct derivation scheme of discrete time-variant RNN (DT-RNN) algorithm for addressing discrete tim...

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Published in: Applied Soft Computing
ISSN: 1568-4946
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62067
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Abstract: The improvement of recurrent neural network (RNN) algorithms is one of target of many researchers, and these algorithms are wieldy used to solve time-variant problems in a variety of domains. A novel direct derivation scheme of discrete time-variant RNN (DT-RNN) algorithm for addressing discrete time-variant matrix pseudo-inversion is discussed in this paper. To be more specific, firstly, a DT-RNN algorithm mathematically founded on the second-order Taylor expansion is proposed for dealing with discrete time-variant matrix pseudo-inversion, and it does not require the theoretical support of continuous time-variant RNN (CT-RNN) algorithm. Secondly, the results of theoretical analyses of the proposed DT-RNN algorithm are also presented in this paper. These results demonstrate that the novel DT-RNN algorithm has remarkable computing performance. The efficiency and applicability of the DT-RNN algorithm have been verified through one numerical experiment example and two robotic manipulator experiments.
College: Faculty of Science and Engineering
Funders: This work was supported by the National Natural Science Foundation of China (with numbers 61906164 and 61972335), by the Natural Science Foundation of Jiangsu Province of China (with number BK20190875), by the Six Talent Peaks Project in Jiangsu Province (with number RJFW-053), by Jiangsu “333” Project, by Qinglan project of Yangzhou University, by High-end Talent Support Program of Yangzhou University, by the Cross-Disciplinary Project of the Animal Science Special Discipline of Yangzhou University, and by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (with numbers KYCX21_3234 and SJCX22_1709).
Start Page: 109861

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