Centralized and decentralized global outer-synchronization of asymmetric recurrent time-varying neural network by data-sampling

被引：13
作者：
Lu, Wenlian [1 ,2 ,3 ]
Zheng, Ren [3 ]
Chen, Tianping [3 ,4 ]
机构：
[1] JinLing Hosp Nanjing, Dept Radiol, Nanjing, Jiangsu, Peoples R China
[2] Fudan Univ, Jinling Hosp, Computat Translat Med Ctr, Ctr Computat Syst Biol, Shanghai 200433, Peoples R China
[3] Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China
[4] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China
来源：
NEURAL NETWORKS | 2016年 / 75卷
关键词：
Outer-synchronization; Data sampling; Centralized principle; Decentralized principle; Recurrent neural networks; ABSOLUTE STABILITY; STATE ESTIMATION; EXPONENTIAL SYNCHRONIZATION; MULTIAGENT SYSTEMS; GRADED RESPONSE; STABILIZATION; CONVERGENCE; CONSENSUS;
D O I：
10.1016/j.neunet.2015.11.006
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we discuss outer-synchronization of the asymmetrically connected recurrent time-varying neural networks. By using both centralized and decentralized discretization data sampling principles, we derive several sufficient conditions based on three vector norms to guarantee that the difference of any two trajectories starting from different initial values of the neural network converges to zero. The lower bounds of the common time intervals between data samples in centralized and decentralized principles are proved to be positive, which guarantees exclusion of Zeno behavior. A numerical example is provided to illustrate the efficiency of the theoretical results. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码：22 / 31
页数：10
相关论文
共 41 条
  • [1] CHARACTERISTICS OF RANDOM NETS OF ANALOG NEURON-LIKE ELEMENTS
    AMARI, S
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1972, SMC2 (05): : 643 - &
  • [2] CHARACTERISTICS OF RANDOMLY CONNECTED THRESHOLD-ELEMENT NETWORKS AND NETWORK SYSTEMS
    AMARI, SI
    [J]. PROCEEDINGS OF THE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS, 1971, 59 (01): : 35 - &
  • [3] Stability analysis of delayed cellular neural networks
    Cao, JD
    Zhou, DM
    [J]. NEURAL NETWORKS, 1998, 11 (09) : 1601 - 1605
  • [4] Exponential synchronization of Markovian jumping neural networks with partly unknown transition probabilities via stochastic sampled-data control
    Chandrasekar, A.
    Rakkiyappan, R.
    Rihan, Fathalla A.
    Lakshmanan, S.
    [J]. NEUROCOMPUTING, 2014, 133 : 385 - 398
  • [5] Stability of asymmetric Hopfield networks
    Chen, TP
    Amari, SI
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2001, 12 (01): : 159 - 163
  • [6] Chen TP, 2002, ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, P372
  • [7] Convergence of delayed dynamical systems
    Chen, TP
    [J]. NEURAL PROCESSING LETTERS, 1999, 10 (03) : 267 - 271
  • [8] Exponential convergence of delayed dynamical systems
    Chen, TP
    Amari, S
    [J]. NEURAL COMPUTATION, 2001, 13 (03) : 621 - 635
  • [9] ABSOLUTE STABILITY OF GLOBAL PATTERN-FORMATION AND PARALLEL MEMORY STORAGE BY COMPETITIVE NEURAL NETWORKS
    COHEN, MA
    GROSSBERG, S
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1983, 13 (05): : 815 - 826
  • [10] Envelope-constrained H∞ filtering with fading measurements and randomly occurring nonlinearities: The finite horizon case
    Ding, Derui
    Wang, Zidong
    Shen, Bo
    Dong, Hongli
    [J]. AUTOMATICA, 2015, 55 : 37 - 45
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