Novel discontinuous control for exponential synchronization of memristive recurrent neural networks with heterogeneous time-varying delays

被引：28
作者：
Zhang, Ruimei [1 ]
Zeng, Deqiang [2 ]
Park, Ju H. [3 ]
Zhong, Shouming [1 ]
Yu, Yongbin [4 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China
[2] Neijiang Normal Univ, Data Recovery Key Lab Sichuan Prov, Numer Simulat Key Lab Sichuan Prov, Neijiang 641100, Sichuan, Peoples R China
[3] Yeungnam Univ, Dept Elect Engn, 280 Daehak Ro, Kyongsan 38541, South Korea
[4] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2018年 / 355卷 / 05期
基金：
新加坡国家研究基金会; 中国国家自然科学基金;
关键词：
COMPLEX DYNAMICAL NETWORKS; ROBUST SYNCHRONIZATION; MIXED DELAYS; SYSTEMS; STABILITY;
D O I：
10.1016/j.jfranklin.2018.01.018
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 investigates the exponential synchronization problem of memristive recurrent neural networks (MRNNs) with heterogeneous time-varying delays (HTVDs). First, a novel discontinuous feedback control is designed, in which a tunable scalar is introduced. The tunable scalar makes the controller more flexible in reducing the upper bound of the control gain. Based on this control scheme, the double integral term can be successfully used to construct the LKF. Second, New method for tackling memristive synaptic weights and new estimation technique are presented. Third, based on the LKF and estimation technique, synchronization criterion is derived. In comparison with existing results, the established criterion is less conservatism thanks to the double integral term of the LKF. Finally, numerical simulations are presented to validate the effectiveness and advantages of the proposed results. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2826 / 2848
页数：23
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