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Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation
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Long Jin ,
Shuai Li ,
Jipeng Qiang
Journal of the Franklin Institute, Volume: 357, Issue: 6, Pages: 3636 - 3655
Swansea University Author:
Shuai Li
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DOI (Published version): 10.1016/j.jfranklin.2020.02.024
Abstract
In this paper, a novel discrete-time advance zeroing neural network (DT-AZNN) model is proposed, developed and investigated for solving future augmented Sylvester matrix equation (F-ASME). First of all, based on the advance zeroing neural network (AZNN) design formula, a novel continuous-time advanc...
| Published in: | Journal of the Franklin Institute |
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| ISSN: | 0016-0032 |
| Published: |
Elsevier BV
2020
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa53642 |
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| Abstract: |
In this paper, a novel discrete-time advance zeroing neural network (DT-AZNN) model is proposed, developed and investigated for solving future augmented Sylvester matrix equation (F-ASME). First of all, based on the advance zeroing neural network (AZNN) design formula, a novel continuous-time advance zeroing neural network (CT-AZNN) model is shown for solving continuous-time augmented Sylvester matrix equation (CT-ASME). Secondly, a recently published discretization formula is further investigated with the optimal sampling gap of the discretization formula proposed. Then, for solving F-ASME, a novel DT-AZNN model is proposed based on the discretization formula. Theoretical analyses on the convergence property and the perturbation suppression performance of the DT-AZNN model are provided. Moreover, comparative numerical experimental results are conducted to prove the effectiveness and robustness of the proposed DT-AZNN model for solving F-ASME. |
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| Keywords: |
Future augmented Sylvester matrix equation, Zeroing neural network, Discretization formula, Robustness |
| College: |
Faculty of Science and Engineering |
| Issue: |
6 |
| Start Page: |
3636 |
| End Page: |
3655 |