Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural Networks

Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural Networks

The memristor as the fourth circuit element, it can capture some key aspects of biological synaptic plasticity. So, it is significant that the characteristic of memristors is considered in neural networks. This paper investigates input-to-state stability (ISS) of a class of memristive simplified Coh...

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Main Authors: Yong Zhao, Shanshan Ren
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/3612394
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spelling doaj-4f07ef3adb9f431ba7a22e5fee6e7ab52020-11-25T01:24:52ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/36123943612394Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural NetworksYong Zhao0Shanshan Ren1School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo 454000, ChinaThe memristor as the fourth circuit element, it can capture some key aspects of biological synaptic plasticity. So, it is significant that the characteristic of memristors is considered in neural networks. This paper investigates input-to-state stability (ISS) of a class of memristive simplified Cohen–Grossberg bidirectional associative memory (BAM) neural networks with variable time delays. In the sense of Filippov solution, some novel sufficient criteria for ISS are obtained based on differential inclusions and differential inequalities; when the input is zero, the stability of the total system is state stable. Furthermore, numerical simulations are illustrated to show the feasibility of our results.http://dx.doi.org/10.1155/2020/3612394
collection DOAJ
language English
format Article
sources DOAJ
author Yong Zhao
Shanshan Ren
spellingShingle Yong Zhao
Shanshan Ren
Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural Networks
Complexity
author_facet Yong Zhao
Shanshan Ren
author_sort Yong Zhao
title Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural Networks
title_short Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural Networks
title_full Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural Networks
title_fullStr Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural Networks
title_full_unstemmed Novel Criteria of ISS Analysis for Delayed Memristive Simplified Cohen–Grossberg BAM Neural Networks
title_sort novel criteria of iss analysis for delayed memristive simplified cohen–grossberg bam neural networks
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2020-01-01
description The memristor as the fourth circuit element, it can capture some key aspects of biological synaptic plasticity. So, it is significant that the characteristic of memristors is considered in neural networks. This paper investigates input-to-state stability (ISS) of a class of memristive simplified Cohen–Grossberg bidirectional associative memory (BAM) neural networks with variable time delays. In the sense of Filippov solution, some novel sufficient criteria for ISS are obtained based on differential inclusions and differential inequalities; when the input is zero, the stability of the total system is state stable. Furthermore, numerical simulations are illustrated to show the feasibility of our results.
url http://dx.doi.org/10.1155/2020/3612394
work_keys_str_mv AT yongzhao novelcriteriaofissanalysisfordelayedmemristivesimplifiedcohengrossbergbamneuralnetworks
AT shanshanren novelcriteriaofissanalysisfordelayedmemristivesimplifiedcohengrossbergbamneuralnetworks
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