Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive Effect | Zendy

Guowei Yang | Zendy; Yonggui Kao | Zendy; Changhong Wang | Zendy

Open Access

Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive Effect

Author(s) -

Guowei Yang,

Yonggui Kao,

Changhong Wang

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 - 10.1155/2013/645262

Subject(s) - exponential stability , mathematics , differentiable function , cellular neural network , reaction–diffusion system , convergence (economics) , artificial neural network , stability (learning theory) , exponential function , diffusion , boundary (topology) , mathematical analysis , computer science , artificial intelligence , machine learning , nonlinear system , physics , quantum mechanics , economics , thermodynamics , economic growth

This paper considers dynamical behaviors of a class of fuzzyimpulsive reaction-diffusion delayed cellular neural networks(FIRDDCNNs) with time-varying periodic self-inhibitions,interconnection weights, and inputs. By using delay differentialinequality, M-matrix theory, and analytic methods, some newsufficient conditions ensuring global exponential stability of theperiodic FIRDDCNN model with Neumann boundary conditions areestablished, and the exponential convergence rate index isestimated. The differentiability of the time-varying delays is notneeded. An example is presented to demonstrate the efficiency andeffectiveness of the obtained results

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