Global Mittag-Leffler Synchronization for Fractional-Order BAM Neural Networks with Impulses and Multiple Variable Delays via Delayed-Feedback Control Strategy

被引：50
作者：
Ye, Renyu [1 ,2 ]
Liu, Xinsheng [1 ,2 ]
Zhang, Hai [3 ]
Cao, Jinde [4 ,5 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, State Key Lab Mech & Control Mech Struct, Inst Nano Sci, Nanjing 210018, Jiangsu, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Dept Math, Nanjing 210018, Jiangsu, Peoples R China
[3] Anqing Normal Univ, Sch Math & Computat Sci, Anqing 246133, Peoples R China
[4] Southeast Univ, Sch Math, Nanjing 210096, Jiangsu, Peoples R China
[5] Nantong Univ, Sch Elect Engn, Nantong 226000, Peoples R China
来源：
NEURAL PROCESSING LETTERS | 2019年 / 49卷 / 01期
关键词：
Mittag-Leffler synchronization; Delayed-feedback control; Lyapunov functionals; Fractional BAM neural networks; Time-varying delays; Impulsive effects; FINITE-TIME SYNCHRONIZATION; STABILITY ANALYSIS; CLUSTER SYNCHRONIZATION; DIFFERENTIAL-EQUATIONS; EXPONENTIAL STABILITY; DYNAMICS;
D O I：
10.1007/s11063-018-9801-0
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 is concerned with the global Mittag-Leffler synchronization schemes for the Caputo type fractional-order BAM neural networks with multiple time-varying delays and impulsive effects. Based on the delayed-feedback control strategy and Lyapunov functional approach, the sufficient conditions are established to ensure the global Mittag-Leffler synchronization, which are described as the algebraic inequalities associated with the network parameters. The control gain constants can be searched in a wider range following the proposed synchronization conditions. The obtained results are more general and less conservative. A numerical example is also presented to illustrate the feasibility and effectiveness of the theoretical results based on the modified predictor-corrector algorithm.
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页码：1 / 18
页数：18
相关论文
共 52 条
[1]  
[Anonymous], 2012, FRACTIONAL DYNAMICS
[2]   Piecewise Pseudo Almost Periodic Solution for Impulsive Generalised High-Order Hopfield Neural Networks with Leakage Delays [J].
Aouiti, Chaouki ;
M'hamdi, Mohammed Salah ;
Cao, Jinde ;
Alsaedi, Ahmed .
NEURAL PROCESSING LETTERS, 2017, 45 (02) :615-648
[3]   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
[4]   Synchronization of fractional-order complex-valued neural networks with time delay [J].
Bao, Haibo ;
Park, Ju H. ;
Cao, Jinde .
NEURAL NETWORKS, 2016, 81 :16-28
[5]  
Bhalekar S., 2011, J. Fract. Calc. Appl., V1, P1, DOI DOI 10.1155/2011/250763
[6]   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
[7]   Finite-time Stability of Fractional-order Complex-valued Neural Networks with Time Delays [J].
Ding, Xiaoshuai ;
Cao, Jinde ;
Zhao, Xuan ;
Alsaadi, Fuad E. .
NEURAL PROCESSING LETTERS, 2017, 46 (02) :561-580
[8]   Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller [J].
Ding, Zhixia ;
Shen, Yi .
NEURAL NETWORKS, 2016, 76 :97-105
[9]   Using general quadratic Lyapunov functions to prove Lyapunov uniform stability for fractional order systems [J].
Duarte-Mermoud, Manuel A. ;
Aguila-Camacho, Norelys ;
Gallegos, Javier A. ;
Castro-Linares, Rafael .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2015, 22 (1-3) :650-659
[10]   Nonlinear Measure Approach for the Stability Analysis of Complex-Valued Neural Networks [J].
Gong, Weiqiang ;
Liang, Jinling ;
Zhang, Congjun ;
Cao, Jinde .
NEURAL PROCESSING LETTERS, 2016, 44 (02) :539-554
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