Synchronization of Fractional Order Uncertain BAM Competitive Neural Networks

M. Syed Ali; M. Hymavathi; Syeda Asma Kauser; Grienggrai Rajchakit; Porpattama Hammachukiattikul; Nattakan Boonsatit

doi:10.3390/fractalfract6010014

Synchronization of Fractional Order Uncertain BAM Competitive Neural Networks

M. Syed Ali
, M. Hymavathi
, Syeda Asma Kauser
, Grienggrai Rajchakit
, Porpattama Hammachukiattikul
, Nattakan Boonsatit

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

17 Scopus citations

Abstract

This article examines the drive-response synchronization of a class of fractional order uncertain BAM (Bidirectional Associative Memory) competitive neural networks. By using the differential inclusions theory, and constructing a proper Lyapunov-Krasovskii functional, novel sufficient conditions are obtained to achieve global asymptotic stability of fractional order uncertain BAM competitive neural networks. This novel approach is based on the linear matrix inequality (LMI) technique and the derived conditions are easy to verify via the LMI toolbox. Moreover, numerical examples are presented to show the feasibility and effectiveness of the theoretical results.

Original language	English
Article number	14
Journal	Fractal and Fractional
Volume	6
Issue number	1
DOIs	https://doi.org/10.3390/fractalfract6010014
State	Published - Jan 2022
Externally published	Yes

Keywords

Competitive neural network
Fractional order
Lyapunov–Krasovskii functional
Synchronization

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10.3390/fractalfract6010014

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title = "Synchronization of Fractional Order Uncertain BAM Competitive Neural Networks",

abstract = "This article examines the drive-response synchronization of a class of fractional order uncertain BAM (Bidirectional Associative Memory) competitive neural networks. By using the differential inclusions theory, and constructing a proper Lyapunov-Krasovskii functional, novel sufficient conditions are obtained to achieve global asymptotic stability of fractional order uncertain BAM competitive neural networks. This novel approach is based on the linear matrix inequality (LMI) technique and the derived conditions are easy to verify via the LMI toolbox. Moreover, numerical examples are presented to show the feasibility and effectiveness of the theoretical results.",

keywords = "Competitive neural network, Fractional order, Lyapunov–Krasovskii functional, Synchronization",

author = "Ali, \{M. Syed\} and M. Hymavathi and Kauser, \{Syeda Asma\} and Grienggrai Rajchakit and Porpattama Hammachukiattikul and Nattakan Boonsatit",

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year = "2022",

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doi = "10.3390/fractalfract6010014",

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volume = "6",

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T1 - Synchronization of Fractional Order Uncertain BAM Competitive Neural Networks

AU - Ali, M. Syed

AU - Hymavathi, M.

AU - Kauser, Syeda Asma

AU - Rajchakit, Grienggrai

AU - Hammachukiattikul, Porpattama

AU - Boonsatit, Nattakan

PY - 2022/1

Y1 - 2022/1

N2 - This article examines the drive-response synchronization of a class of fractional order uncertain BAM (Bidirectional Associative Memory) competitive neural networks. By using the differential inclusions theory, and constructing a proper Lyapunov-Krasovskii functional, novel sufficient conditions are obtained to achieve global asymptotic stability of fractional order uncertain BAM competitive neural networks. This novel approach is based on the linear matrix inequality (LMI) technique and the derived conditions are easy to verify via the LMI toolbox. Moreover, numerical examples are presented to show the feasibility and effectiveness of the theoretical results.

AB - This article examines the drive-response synchronization of a class of fractional order uncertain BAM (Bidirectional Associative Memory) competitive neural networks. By using the differential inclusions theory, and constructing a proper Lyapunov-Krasovskii functional, novel sufficient conditions are obtained to achieve global asymptotic stability of fractional order uncertain BAM competitive neural networks. This novel approach is based on the linear matrix inequality (LMI) technique and the derived conditions are easy to verify via the LMI toolbox. Moreover, numerical examples are presented to show the feasibility and effectiveness of the theoretical results.

KW - Competitive neural network

KW - Fractional order

KW - Lyapunov–Krasovskii functional

KW - Synchronization

UR - https://www.scopus.com/pages/publications/85123798736

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DO - 10.3390/fractalfract6010014

M3 - Article

AN - SCOPUS:85123798736

SN - 2504-3110

VL - 6

JO - Fractal and Fractional

JF - Fractal and Fractional

IS - 1

M1 - 14

ER -

Synchronization of Fractional Order Uncertain BAM Competitive Neural Networks

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Keywords

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