Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses | Zendy

Xiaofeng Chen | Zendy; Qiankun Song | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses

Author(s) -

Xiaofeng Chen,

Qiankun Song

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 - 10.1155/2012/168375

Subject(s) - uniqueness , exponential stability , mathematics , linear matrix inequality , stability (learning theory) , control theory (sociology) , discrete time and continuous time , artificial neural network , mathematical optimization , mathematical analysis , computer science , nonlinear system , statistics , physics , control (management) , quantum mechanics , machine learning , artificial intelligence

The problem on global exponential stability of antiperiodic solution is investigated for a class of impulsive discrete-time neural networks with time-varying discrete delays and distributed delays. By constructing an appropriate Lyapunov-Krasovskii functional, and using the contraction mapping principle and the matrix inequality techniques, a new delay-dependent criterion for checking the existence, uniqueness, and global exponential stability of anti-periodic solution is derived in linear matrix inequalities (LMIs). Two simulation examples are given to show the effectiveness of the proposed result

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