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Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation

Yang Shi Orcid Logo, Long Jin Orcid Logo, Shuai Li Orcid Logo, Jipeng Qiang

Journal of the Franklin Institute, Volume: 357, Issue: 6, Pages: 3636 - 3655

Swansea University Author: Shuai Li Orcid Logo

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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 advanc...

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Published in: Journal of the Franklin Institute
ISSN: 0016-0032
Published: Elsevier BV 2020
Online Access: Check full text

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.
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