Synchronization for memristive chaotic neural networks using Wirtinger-based multiple integral inequality

被引：1
|
作者：
Zheng, Cheng-De [1 ]
Zhang, Yue [1 ]
Wang, Zhanshan [2 ]
机构：
[1] Dalian Jiaotong Univ, Sch Sci, Dalian, Peoples R China
[2] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
来源：
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2018年 / 9卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Memristive neural networks; Synchronization; Wirtinger-based integral inequality; Reciprocally convex combination; Free-matrix-based inequality; TIME-VARYING DELAYS; TO-STATE STABILITY; EXPONENTIAL SYNCHRONIZATION; PROJECTIVE SYNCHRONIZATION; IMPULSIVE CONTROL; MIXED DELAYS; SYSTEMS; CRITERIA;
D O I：
10.1007/s13042-016-0626-8
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 investigates the synchronization problem for a class of memristive chaotic neural networks with time-varying delays. Based on te Wirtinger-based double integral inequality, two novel inequalities are proposed, which are multiple integral forms of the Wirtinger-based integral inequality. Next, by applying the reciprocally convex combination approach for high order case and a free-matrix-based inequality, novel delay-dependent conditions are established to achieve the synchronization for the memristive chaotic neural networks. The results are based on dividing the bounding of activation function into two subintervals with equal length. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical results.
引用
收藏
页码：1069 / 1083
页数：15
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