Antiperiodic dynamical behaviors of discontinuous neutral‐type Cohen‐Grossberg neural networks with mixed time delays. (1st December 2019)

• Record Type: Journal Article
• Authors: Kong, Fanchao; Zhu, Quanxin
• Is Part Of: Computational intelligence. Volume 36:Number 2(2020)
• Page Start: 698
• Page End: 719
• Publication Date: 2019-12-01
• Subjects: antiperiodic solution -- discontinuous activations -- global exponential stability -- mixed time delays -- neutral‐type Cohen‐Grossberg neural network; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12262
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: This article presents a class of neutral‐type Cohen‐Grossberg neural networks with discontinuous activations and mixed time delays. Based on the functional differential inclusions theory, inequality technique, and nonsmooth analysis theory with Lyapunovlike approach, some new sufficient criteria are given to ascertain the existence, uniqueness, and globally exponential stability of the antiperiodic solution. Finally, two topical numerical examples and the corresponding computer simulations are delineated to substantiate the correctness of our theoretical predictions. The obtained results of this article are new and complement some related earlier works.