Global Synchronization and Anti-Synchronization of Fractional-Order Complex-Valued Gene Regulatory Networks With Time-Varying Delays

被引：13

作者：

Wu, Zhaohua ^{[1
,2
]}

Wang, Zhiming ^{[1
,2
]}

Zhou, Tiejun ^{[1
]}

Tan, Siqiao ^{[1
,2
]}

机构：

[1] Hunan Agr Univ, Coll Informat & Intelligence Sci, Changsha 410128, Peoples R China

[2] Hunan Agr Univ, Hunan Engn Res Ctr Informat Technol Agr, Changsha 410128, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷

基金：

中国国家自然科学基金;

关键词：

Synchronization; Delays; Delay effects; Artificial neural networks; Mathematical model; Proteins; Adaptive control; Complex-valued; feedback controller; fractional-order; gene regulatory networks (GRNs); synchronization and anti-synchronization; time-varying delays; BAM NEURAL-NETWORKS; HOPF-BIFURCATION ANALYSIS; STABILITY; DISCRETE; SYSTEMS;

D O I：

10.1109/ACCESS.2020.3016706

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents the analytical and numerical investigation on the global synchronization and anti-synchronization for a class of drive-response systems of fractional-order complex-valued gene regulatory networks with time-varying delays (DFGRNs). In our design, two kinds of adaptive feedback controllers are used to synchronize and anti-synchronize the proposed drive-response systems, and some sufficient conditions on the global asymptotical synchronization and anti-synchronization are given with the methods of the fractional Lyapunov-like functions and the fractional-order inequalities. In the numerical simulations, two minimum "estimated time", T-1 and T-2, are computed to achieve the synchronization and anti-synchronization. We find that T-1 and T-2 increase with the decreasing of the fractional order of DFGRNs.

引用

页码：150555 / 150572

页数：18

共 46 条

[1] Anti-synchronization of fractional order chaotic and hyperchaotic systems with fully unknown parameters using modified adaptive control [J].

Al-Sawalha, M. Mossa ;

Al-Sawalha, Ayman .

OPEN PHYSICS, 2016, 14 (01) :304-313

[2] Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays [J].

Ali, M. Syed ;

Balasubramaniam, P. ;

Zhu, Quanxin .

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2017, 8 (01) :263-273

[3]

[Anonymous], 2014, CHEMOMETR INTELL LAB, DOI DOI 10.1016/J.CHEMOLAB.2014.07.007

[4]

[Anonymous], 2015, NEUROCOMPUTING, DOI DOI 10.1016/J.NEUCOM.2015.02.049

[5]

[Anonymous], 2014, MATH PROBL ENG, DOI DOI 10.1155/2014/302702

[6]

[Anonymous], 2020, APPL MATH COMPUT, DOI DOI 10.1016/J.AMC.2019.125020

[7]

[Anonymous], 2019, PHYSICA A, DOI DOI 10.1016/J.PHYSA.2019.122427

[8]

[Anonymous], 2015, NEUROCOMPUTING, DOI DOI 10.1016/J.NEUCOM.2015.04.064

[9]

[Anonymous], 2006, IEEE IJCNN

[10] Adaptive synchronization of fractional-order memristor-based neural networks with time delay [J].

Bao, Haibo ;

Park, Ju H. ;

Cao, Jinde .

NONLINEAR DYNAMICS, 2015, 82 (03) :1343-1354

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