Global asymptotic stability analysis for neutral-type complex-valued neural networks with random time-varying delays

ABSTRACT

This paper investigates the global asymptotic stability problem for a class of neutral-type complex-valued neural networks with random time-varying delays. By introducing a stochastic variable with Bernoulli distribution, the information of time-varying delay is assumed to be random time-varying delays. By constructing an appropriate Lyapunov–Krasovskii functional and employing inequality technique, several sufficient conditions are obtained to ensure the global asymptotically stability of equilibrium point for the considered neural networks. The obtained stability criterion is expressed in terms of complex-valued linear matrix inequalities, which can be simply solved by effective YALMIP toolbox in MATLAB. Finally, three numerical examples are given to demonstrate the efficiency of the proposed main results.

KEYWORDS:

• Complex-valued neural networks
• Lyapunov–Krasovskii functional
• random time-varying delays
• neutral delay

Acknowledgements

The authors are grateful to the handling editor and reviewers for their valuable suggestions and comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Board for Higher Mathematics of India [grant numbers 02011/10/2019 NBHM (R.P) R & D II/1242].

Notes on contributors

R. Sriraman

R. Sriraman is currently pursuing the Ph.D. degree with the Department of Mathematics, Thiruvalluvar University, Tamil Nadu, India. His current research interests include neural networks and time delay systems.

R. Samidurai

R. Samidurai received the Ph.D. degree in 2010 with the Department of Mathematics, Periyar University, Salem, India. Currently, he is an Assistant Professor with the Department of Mathematics, Thiruvalluvar University, Tamil Nadu, India. He has authored over 50 research papers. He serves as a reviewer of various SCI journals. His research interests include stochastic differential equations, nonlinear systems, impulsive neural networks, networked control systems, control theory in partial differential equations and their applications.