Journal article 683 views
Novel Discrete-Time Recurrent Neural Network for Robot Manipulator: A Direct Discretization Technical Route
,
Bin Li,
Xiaobing Sun
IEEE Transactions on Neural Networks and Learning Systems, Volume: 34, Issue: 6, Pages: 1 - 10
Swansea University Author:
Shuai Li
Full text not available from this repository: check for access using links below.
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 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa58131 |
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| 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 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. |
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| College: |
Faculty of Science and Engineering |
| Issue: |
6 |
| Start Page: |
1 |
| End Page: |
10 |