Boundedness and global robust stability analysis of delayed complex-valued neural networks with interval parameter uncertainties. (July 2018)

• Record Type: Journal Article
• Authors: Song, Qiankun; Yu, Qinqin; Zhao, Zhenjiang; Liu, Yurong; Alsaadi, Fuad E.
• Is Part Of: Neural networks. Volume 103(2018)
• Page Start: 55
• Page End: 62
• Publication Date: 2018-07
• Subjects: Complex-valued neural networks -- Robust stability -- Equilibrium point -- Interval parameter uncertainty -- Delays; 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.2018.03.008
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, the boundedness and robust stability for a class of delayed complex-valued neural networks with interval parameter uncertainties are investigated. By using Homomorphic mapping theorem, Lyapunov method and inequality techniques, sufficient condition to guarantee the boundedness of networks and the existence, uniqueness and global robust stability of equilibrium point is derived for the considered uncertain neural networks. The obtained robust stability criterion is expressed in complex-valued LMI, which can be calculated numerically using YALMIP with solver of SDPT3 in MATLAB. An example with simulations is supplied to show the applicability and advantages of the acquired result.