Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations

Wei Yao; Chunhua Wang; Yichuang Sun; Chao Zhou; Hairong  Lin

doi:10.1016/j.amc.2020.125483

Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations

Wei Yao
, Chunhua Wang
, Yichuang Sun
, Chao Zhou
, Hairong Lin

Research output: Contribution to journal › Article › peer-review

50 Citations (Scopus)

18 Downloads (Pure)

Abstract

Due to instability being induced easily by parameter disturbances of network systems, this paper investigates the multistability of memristive Cohen-Grossberg neural networks (MCGNNs) under stochastic parameter perturbations. It is demonstrated that stable equilibrium points of MCGNNs can be flexibly located in the odd-sequence or even-sequence regions. Some sufficient conditions are derived to ensure the exponential multistability of MCGNNs under parameter perturbations. It is found that there exist at least (w+2) l (or (w+1) l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. In the paper, two numerical examples are given to verify the correctness and effectiveness of the obtained results.

Original language	English
Article number	125483
Number of pages	18
Journal	Applied Mathematics and Computation
Volume	386
Early online date	26 Jun 2020
DOIs	https://doi.org/10.1016/j.amc.2020.125483
Publication status	Published - 1 Dec 2020

Keywords

Exponential multistability
Memristive Cohen-Grossberg neural network
Stable equilibrium point
Stochastic parameter perturbation

Access to Document

10.1016/j.amc.2020.125483Licence: Unspecified

AMC-Exponential multistability of MCGNNs
© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.
Accepted author manuscript, 459 KBLicence: CC BY-NC-ND

Cite this

@article{4f8e89c55b1143ce98290f09ca6041e1,

title = "Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations",

abstract = "Due to instability being induced easily by parameter disturbances of network systems, this paper investigates the multistability of memristive Cohen-Grossberg neural networks (MCGNNs) under stochastic parameter perturbations. It is demonstrated that stable equilibrium points of MCGNNs can be flexibly located in the odd-sequence or even-sequence regions. Some sufficient conditions are derived to ensure the exponential multistability of MCGNNs under parameter perturbations. It is found that there exist at least (w+2) l (or (w+1) l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. In the paper, two numerical examples are given to verify the correctness and effectiveness of the obtained results. ",

keywords = "Exponential multistability, Memristive Cohen-Grossberg neural network, Stable equilibrium point, Stochastic parameter perturbation",

author = "Wei Yao and Chunhua Wang and Yichuang Sun and Chao Zhou and Hairong Lin",

note = "{\textcopyright} 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.",

year = "2020",

month = dec,

day = "1",

doi = "10.1016/j.amc.2020.125483",

language = "English",

volume = "386",

journal = "Applied Mathematics and Computation",

issn = "0096-3003",

publisher = "Elsevier",

}

TY - JOUR

T1 - Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations

AU - Yao, Wei

AU - Wang, Chunhua

AU - Sun, Yichuang

AU - Zhou, Chao

AU - Lin, Hairong

N1 - © 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.

PY - 2020/12/1

Y1 - 2020/12/1

N2 - Due to instability being induced easily by parameter disturbances of network systems, this paper investigates the multistability of memristive Cohen-Grossberg neural networks (MCGNNs) under stochastic parameter perturbations. It is demonstrated that stable equilibrium points of MCGNNs can be flexibly located in the odd-sequence or even-sequence regions. Some sufficient conditions are derived to ensure the exponential multistability of MCGNNs under parameter perturbations. It is found that there exist at least (w+2) l (or (w+1) l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. In the paper, two numerical examples are given to verify the correctness and effectiveness of the obtained results.

AB - Due to instability being induced easily by parameter disturbances of network systems, this paper investigates the multistability of memristive Cohen-Grossberg neural networks (MCGNNs) under stochastic parameter perturbations. It is demonstrated that stable equilibrium points of MCGNNs can be flexibly located in the odd-sequence or even-sequence regions. Some sufficient conditions are derived to ensure the exponential multistability of MCGNNs under parameter perturbations. It is found that there exist at least (w+2) l (or (w+1) l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. In the paper, two numerical examples are given to verify the correctness and effectiveness of the obtained results.

KW - Exponential multistability

KW - Memristive Cohen-Grossberg neural network

KW - Stable equilibrium point

KW - Stochastic parameter perturbation

UR - https://www.scopus.com/pages/publications/85086829566

U2 - 10.1016/j.amc.2020.125483

DO - 10.1016/j.amc.2020.125483

M3 - Article

SN - 0096-3003

VL - 386

JO - Applied Mathematics and Computation

JF - Applied Mathematics and Computation

M1 - 125483

ER -

Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this