Open AccessNovel Criteria on Global Robust Exponential Stability to a Class of Reaction‐Diffusion Neural Networks with Delays
Author(s) -
Jie Pan,
Shouming Zhong
Publication year - 2009
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 - 10.1155/2009/291594
Subject(s) - reaction–diffusion system , exponential stability , artificial neural network , bounded function , constant (computer programming) , exponential function , diffusion , class (philosophy) , invariant (physics) , equilibrium point , mathematics , control theory (sociology) , lyapunov function , stability (learning theory) , computer science , differential equation , control (management) , nonlinear system , mathematical analysis , artificial intelligence , physics , machine learning , quantum mechanics , mathematical physics , thermodynamics , programming language
The global exponential robust stability is investigated to a class of reaction-diffusion Cohen-Grossberg neural network (CGNNs) with constant time-delays, this neural network contains timeinvariant uncertain parameters whose values are unknown but bounded in given compact sets. Byemploying the Lyapunov-functional method, several new sufficient conditions are obtained to ensure theglobal exponential robust stability of equilibrium point for the reaction diffusion CGNN with delays.These sufficient conditions depend on the reaction-diffusion terms, which is a preeminent featurethat distinguishes the present research from the previous research on delayed neural networks withreaction-diffusion. Two examples are given to show the effectiveness of the obtained results
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