Exponential stability of Markovian jumping Cohen–Grossberg neural networks with mixed mode-dependent time-delays

• Published in: Neurocomputing (Amsterdam) Vol. 177; pp. 409 - 415
• Main Authors: Liu, Yurong, Liu, Weibo, Obaid, Mustafa Ali, Abbas, Ibrahim Atiatallah
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 12.02.2016
• Subjects: Cohen–Grossberg neural networks; Exponential stability; Markovian jumping parameters; Mixed mode-dependent time-delays; Markovian jumping parameters; Mixed mode-dependent time-delays; Exponential stability; Cohen–Grossberg neural networks
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2015.11.046

In this paper, the exponential stability problem is investigated for a class of Cohen–Grossberg neural networks with Markovian jumping parameter and mixed time-delays. The mixed time-delays under consideration consist of both the mode-dependent discrete time-delays and the mode-dependent distributed time-delays. By constructing a new Lyapunov–Krasovskii functional and employing the stochastic analysis techniques, sufficient conditions are proposed to guarantee that the addressed neural networks are exponentially stable in the mean square sense. It is shown that the developed stability criteria can be easily verified by using the standard numerical software. Finally, an illustrative example is provided to show the feasibility and usefulness of the developed results.