Multistability in Mittag-Leffler sense of fractional-order neural networks with piecewise constant arguments

被引:26
作者
Wan, Liguang [1 ]
Wu, Ailong [2 ,3 ]
机构
[1] Hubei Normal Univ, Coll Mechatron & Control Engn, Huangshi 435002, Peoples R China
[2] Hubei Normal Univ, Coll Math & Stat, Huangshi 435002, Peoples R China
[3] Xi An Jiao Tong Univ, Inst Informat & Syst Sci, Xian 710049, Shaanxi, Peoples R China
关键词
Fractional-order neural networks; Multistability; Piecewise constant arguments; TIME-VARYING DELAYS; GLOBAL EXPONENTIAL STABILITY; ACTIVATION FUNCTIONS; ASYMPTOTIC STABILITY; ROBUST STABILITY; LEAKAGE DELAYS; STABILIZATION; DISCRETE; DYNAMICS; EQUATIONS;
D O I
10.1016/j.neucom.2018.01.049
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper discusses the multistability in Mittag-Leffler sense of fractional-order neural networks with piecewise constant arguments. According to the boundedness of activation functions and the model of fractional-order neural networks with piecewise constant arguments, n pairs of bounded functions are constructed. On the basis of the sign of the n pairs of bounded functions, the n-dimensional state space is divided into Pi(n)(i=1) (2L(i) + 1) regions. Sufficient conditions are derived to ensure that there exists at leat one equilibrium point in each one of these regions. In addition, Pi(n)(i=1) (L-i + 1) equilibrium points are locally Mittag-Leffler stable. Two numerical examples are provided to demonstrate the validity of the theoretical results. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:1 / 10
页数:10
相关论文
共 54 条
[1]   Stability analysis of recurrent neural networks with piecewise constant argument of generalized type [J].
Akhmet, M. U. ;
Arugaslan, D. ;
Yilmaz, E. .
NEURAL NETWORKS, 2010, 23 (07) :805-811
[2]   Impulsive Hopfield-type neural network system with piecewise constant argument [J].
Akhmet, M. U. ;
Yilmaz, E. .
NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS, 2010, 11 (04) :2584-2593
[3]   Stability in cellular neural networks with a piecewise constant argument [J].
Akhmet, M. U. ;
Arugaslan, D. ;
Yilmaz, E. .
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2010, 233 (09) :2365-2373
[4]   FRACTIONAL ORDER STATE-EQUATIONS FOR THE CONTROL OF VISCOELASTICALLY DAMPED STRUCTURES [J].
BAGLEY, RL ;
CALICO, RA .
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 1991, 14 (02) :304-311
[5]   Global asymptotical ω-periodicity of a fractional-order non-autonomous neural networks [J].
Chen, Boshan ;
Chen, Jiejie .
NEURAL NETWORKS, 2015, 68 :78-88
[6]   Razumikhin-type stability theorems for functional fractional-order differential systems and applications [J].
Chen, Boshan ;
Chen, Jiejie .
APPLIED MATHEMATICS AND COMPUTATION, 2015, 254 :63-69
[7]   Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks [J].
Chen, Jiejie ;
Zeng, Zhigang ;
Jiang, Ping .
NEURAL NETWORKS, 2014, 51 :1-8
[8]   Dynamic analysis of a class of fractional-order neural networks with delay [J].
Chen, Liping ;
Chai, Yi ;
Wu, Ranchao ;
Ma, Tiedong ;
Zhai, Houzhen .
NEUROCOMPUTING, 2013, 111 :190-194
[9]   Stability criterion for delayed neural networks via Wirtinger-based multiple integral inequality [J].
Ding, Sanbo ;
Wang, Zhanshan ;
Wu, Yanming ;
Zhang, Huaguang .
NEUROCOMPUTING, 2016, 214 :53-60
[10]   Delay-dependent multistability in recurrent neural networks [J].
Huang, Gan ;
Cao, Jinde .
NEURAL NETWORKS, 2010, 23 (02) :201-209