Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales | Zendy

Yongkun Li | Zendy; Yuchun Hua | Zendy; Yu Fei | Zendy

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Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales

Author(s) -

Yongkun Li,

Yuchun Hua,

Yu Fei

Publication year - 2009

Publication title -

journal of inequalities and applications

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.735

H-Index - 50

eISSN - 1029-242X

pISSN - 1025-5834

DOI - 10.1155/2009/491268

Subject(s) - mathematics , exponential stability , stability (learning theory) , artificial neural network , exponential function , mathematical analysis , control theory (sociology) , artificial intelligence , physics , computer science , machine learning , nonlinear system , control (management) , quantum mechanics

Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory (BAM) neural networks with distributed delays and impulses on time scales. This is the first time applying the time-scale calculus theory to unify the discrete-time and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework

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