Global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects. (July 2016)

• Record Type: Journal Article
• Authors: Song, Qiankun; Yan, Huan; Zhao, Zhenjiang; Liu, Yurong
• Is Part Of: Neural networks. Volume 79(2016)
• Page Start: 108
• Page End: 116
• Publication Date: 2016-07
• Subjects: Complex-valued neural networks -- Time-varying delay -- Impulsive effects -- Exponential stability -- Complex-valued linear matrix inequality; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2016.03.007
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, the global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects is discussed. By employing Lyapunov functional method and using matrix inequality technique, several sufficient conditions in complex-valued linear matrix inequality form are obtained to ensure the existence, uniqueness and global exponential stability of equilibrium point for the considered neural networks. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The proposed stability results are less conservative than some recently known ones in the literatures, which is demonstrated via two examples with simulations.