Journal article 662 views
Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route
IEEE Transactions on Neural Networks and Learning Systems, Volume: 34, Issue: 6, Pages: 1 - 10
Swansea University Author: Shuai Li
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DOI (Published version): 10.1109/tnnls.2021.3108050
Abstract
Controlling and processing of time-variant problem is universal in the fields of engineering and science, and the discrete-time recurrent neural network (RNN) model has been proven as an effective method for handling a variety of discrete time-variant problems. However, such model usually originates...
Published in: | IEEE Transactions on Neural Networks and Learning Systems |
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ISSN: | 2162-237X 2162-2388 |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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URI: | https://cronfa.swan.ac.uk/Record/cronfa58131 |
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v2 58131 2021-09-28 Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-09-28 MECH Controlling and processing of time-variant problem is universal in the fields of engineering and science, and the discrete-time recurrent neural network (RNN) model has been proven as an effective method for handling a variety of discrete time-variant problems. However, such model usually originates from the discretization research of continuous time-variant problem, and there is little research on the direct discretization method. To address the aforementioned problem, this article introduces a novel discrete-time RNN model for solving the discrete time-variant problem in a pioneering manner. Specifically, a discrete time-variant nonlinear system, which originates from the mathematical modeling of serial robot manipulator, is presented as a target problem. For solving the problem, first, the technique of second-order Taylor expansion is used to deal with the discrete time-variant nonlinear system, and the novel discrete-time RNN model is proposed subsequently. Second, the theoretical analyses are investigated and developed, which shows the convergence and precision of the proposed discrete-time RNN model. Furthermore, three distinct numerical experiments verify the excellent performance of the proposed discrete-time RNN model. In addition, a robot manipulator example further verifies the effectiveness and practicability of the proposed novel discrete-time RNN model. Journal Article IEEE Transactions on Neural Networks and Learning Systems 34 6 1 10 Institute of Electrical and Electronics Engineers (IEEE) 2162-237X 2162-2388 15 9 2021 2021-09-15 10.1109/tnnls.2021.3108050 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2023-06-29T15:17:43.0009838 2021-09-28T14:41:03.1213282 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Yang Shi 1 Wenhan Zhao 2 Shuai Li 0000-0001-8316-5289 3 Bin Li 4 Xiaobing Sun 5 |
title |
Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route |
spellingShingle |
Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route Shuai Li |
title_short |
Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route |
title_full |
Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route |
title_fullStr |
Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route |
title_full_unstemmed |
Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route |
title_sort |
Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route |
author_id_str_mv |
42ff9eed09bcd109fbbe484a0f99a8a8 |
author_id_fullname_str_mv |
42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li |
author |
Shuai Li |
author2 |
Yang Shi Wenhan Zhao Shuai Li Bin Li Xiaobing Sun |
format |
Journal article |
container_title |
IEEE Transactions on Neural Networks and Learning Systems |
container_volume |
34 |
container_issue |
6 |
container_start_page |
1 |
publishDate |
2021 |
institution |
Swansea University |
issn |
2162-237X 2162-2388 |
doi_str_mv |
10.1109/tnnls.2021.3108050 |
publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
college_str |
Faculty of Science and Engineering |
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|
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facultyofscienceandengineering |
hierarchy_top_title |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
department_str |
School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering |
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0 |
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description |
Controlling and processing of time-variant problem is universal in the fields of engineering and science, and the discrete-time recurrent neural network (RNN) model has been proven as an effective method for handling a variety of discrete time-variant problems. However, such model usually originates from the discretization research of continuous time-variant problem, and there is little research on the direct discretization method. To address the aforementioned problem, this article introduces a novel discrete-time RNN model for solving the discrete time-variant problem in a pioneering manner. Specifically, a discrete time-variant nonlinear system, which originates from the mathematical modeling of serial robot manipulator, is presented as a target problem. For solving the problem, first, the technique of second-order Taylor expansion is used to deal with the discrete time-variant nonlinear system, and the novel discrete-time RNN model is proposed subsequently. Second, the theoretical analyses are investigated and developed, which shows the convergence and precision of the proposed discrete-time RNN model. Furthermore, three distinct numerical experiments verify the excellent performance of the proposed discrete-time RNN model. In addition, a robot manipulator example further verifies the effectiveness and practicability of the proposed novel discrete-time RNN model. |
published_date |
2021-09-15T15:17:38Z |
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1770046891137957888 |
score |
11.030361 |