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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Online Access: | http://dx.doi.org/10.1155/2020/3612394 |
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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 |
_version_ |
1715777079214080000 |