New LMI constraint-based settling-time estimation for finite-time stability of fractional-order neural networks-Reference-Cited by-同舟云学术

New LMI constraint-based settling-time estimation for finite-time stability of fractional-order neural networks

Published:2024-01-09 Issue:1 Volume:25 Page:1-18
ISSN:2752-2334
Container-title:Journal of Nonlinear, Complex and Data Science
language:en
Short-container-title:

Author:

Muthu Shafiya¹²,Gnaneswaran Nagamani¹

Affiliation:

1. Department of Mathematics , The Gandhigram Rural Institute (Deemed to be University) , Gandhigram , 624 302 , Tamil Nadu , India

2. Department of Mathematics , College of Engineering and Technology, SRM Institute of Science and Technology , Kattankulathur , 603 203 , Tamil Nadu , India

Abstract

Abstract This study aims to analyze the finite-time stability performance of both non-delayed and delayed fractional-order neural networks. Our primary aim is to investigate the finite-time stability characteristics by introducing a novel inequality designed for estimating the settling time. This fresh inequality serves as the foundation for establishing sufficient criteria, formulated as linear matrix inequalities, which guarantee the finite-time stability of both non-delayed and delayed fractional-order neural networks. Additionally, we underscore the importance of incorporating comprehensive information regarding the lower and upper bounds of the activation function, especially in the context of the proposed non-delayed model. Unlike the previous works, in this paper, the linear matrix inequality technique has been adopted towards the finite-time stability behavior of the proposed model. At last, some numerical examples are examined to validate the efficacy and conservatism of the presented approach and established theoretical results over the existing literature.

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/jncds-2023-0020/pdf

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