Exponential Lag Synchronization and Global Dissipativity for Delayed Fuzzy Cohen-Grossberg Neural Networks with Discontinuous Activations

被引：11
|
作者：
Abdelaziz, Meryem [1 ]
Cherif, Farouk [2 ]
机构：
[1] Univ Sousse, Ecole Super Sci & Technol Hammam Sousse, Lab Math Phys Specials Funct & Applicat LR11ES35, Hammam Sousse 4011, Tunisia
[2] Univ Sousse, Dept Comp Sci ISSATS, Ecole Super Sci & Technol, Lab Math Phys Specials Funct & Applicat LR11ES35, Sousse 4002, Tunisia
来源：
NEURAL PROCESSING LETTERS | 2020年 / 51卷 / 02期
关键词：
Fuzzy Cohen-Grossberg neural networks; Discontinuous neuron activations; Exponential lag synchronization; Global dissipativity; Globally attractive set; LIMIT-CYCLES; PERIODIC SYNCHRONIZATION; STABILITY; SYSTEMS; CONVERGENCE; EXISTENCE;
D O I：
10.1007/s11063-019-10169-8
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 qualitative behavior of a new class of fuzzy Cohen-Grossberg neural networks with discontinuous neuron activations and mixed delays. Roughly speaking, some novel sufficient conditions are established in order to demonstrate the global dissipativity and the exponential lag synchronization of the considered model by using the theory of Filippov systems and Lyapunov method. Finally, two examples are presented to show the effectiveness of the obtained results.
引用
收藏
页码：1653 / 1676
页数：24
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