Existence and Exponential Stability of Solutions for Stochastic Cellular Neural Networks with Piecewise Constant Argument | Zendy

Xiaoai Li | Zendy

Open Access

Existence and Exponential Stability of Solutions for Stochastic Cellular Neural Networks with Piecewise Constant Argument

Author(s) -

Xiaoai Li

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/145061

Subject(s) - piecewise , constant (computer programming) , exponential stability , uniqueness , mathematics , artificial neural network , equilibrium point , moment (physics) , argument (complex analysis) , stability (learning theory) , lyapunov function , cellular neural network , stochastic differential equation , computer science , differential equation , mathematical analysis , nonlinear system , artificial intelligence , machine learning , physics , biochemistry , chemistry , classical mechanics , quantum mechanics , programming language

By using the concept of differential equations with piecewise constant argument of generalized type, a model of stochastic cellular neural networks with piecewise constant argument is developed. Sufficient conditions are obtained for the existence and uniqueness of the equilibrium point for the addressed neural networks. pth moment exponential stability is investigated by means of Lyapunov functional, stochastic analysis, and inequality technique. The results in this paper improve and generalize some of the previous ones. An example with numerical simulations is given to illustrate our results

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