Asymptotic Stability of Impulsive Cellular Neural Networks with Infinite Delays via Fixed Point Theory | Zendy

Yutian Zhang | Zendy; Yuanhong Guan | Zendy

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Asymptotic Stability of Impulsive Cellular Neural Networks with Infinite Delays via Fixed Point Theory

Author(s) -

Yutian Zhang,

Yuanhong Guan

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/427827

Subject(s) - uniqueness , mathematics , exponential stability , fixed point theorem , differentiable function , fixed point , class (philosophy) , stability (learning theory) , equilibrium point , artificial neural network , cellular neural network , point (geometry) , control theory (sociology) , mathematical analysis , differential equation , nonlinear system , control (management) , computer science , physics , geometry , quantum mechanics , artificial intelligence , machine learning

We employ the new method of fixed point theory to study the stability of a class of impulsive cellular neural networks with infinite delays. Some novel and concise sufficient conditions are presented ensuring the existence and uniqueness of solution and the asymptotic stability of trivial equilibrium at the same time. These conditions are easily checked and do not require the boundedness and differentiability of delays

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