Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8253372 | Chaos, Solitons & Fractals | 2018 | 6 Pages |
Abstract
In this paper, we discussed the input-to-state stability of a class of memristive Cohen-Grossberg-type neural networks with variable time delays. Based on a nonsmooth analysis and set-valued maps, some novel sufficient conditions are obtained for the input-to-state stability of such networks, which include some known results as particular cases. Especially, when the input is zero, it reduced to asymptotical stability of the state. Finally, an illustrative example is presented to illustrate the feasibility and effectiveness of our results.
Keywords
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Statistical and Nonlinear Physics
Authors
Yong Zhao, Jürgen Kurths, Lixia Duan,