Stability in Switched Cohen‐Grossberg Neural Networks with Mixed Time Delays and Non‐Lipschitz Activation Functions | Zendy

Huaiqin Wu | Zendy; Guohua Xu | Zendy; Chongyang Wu | Zendy; Ning Li | Zendy; Kewang Wang | Zendy; Qiangqiang Guo | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Stability in Switched Cohen‐Grossberg Neural Networks with Mixed Time Delays and Non‐Lipschitz Activation Functions

Author(s) -

Huaiqin Wu,

Guohua Xu,

Chongyang Wu,

Ning Li,

Kewang Wang,

Qiangqiang Guo

Publication year - 2012

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2012/435402

Subject(s) - dwell time , lipschitz continuity , exponential stability , artificial neural network , inverse , mathematics , linear matrix inequality , stability (learning theory) , control theory (sociology) , computer science , pure mathematics , mathematical optimization , artificial intelligence , physics , medicine , machine learning , clinical psychology , geometry , control (management) , nonlinear system , quantum mechanics

The stability for the switched Cohen-Grossberg neural networkswith mixed time delays and α-inverse Hölder activation functions is investigatedunder the switching rule with the average dwell time property. By applying multiple Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI) technique, a delay-dependent sufficient criterion is achieved to ensure such switchedneural networks to be globally exponentially stable in terms of LMIs, and the exponential decay estimation is explicitly developed for the states too. Two illustrative examples are given to demonstrate the validity of the theoretical results

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research