Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive EffectReport as inadecuate




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Abstract and Applied AnalysisVolume 2013 2013, Article ID 645262, 9 pages

Research Article

College of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China

Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China

Received 3 September 2012; Accepted 4 January 2013

Academic Editor: Tingwen Huang

Copyright © 2013 Guowei Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper considers dynamical behaviors of a class of fuzzyimpulsive reaction-diffusion delayed cellular neural networksFIRDDCNNs with time-varying periodic self-inhibitions,interconnection weights, and inputs. By using delay differentialinequality -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.





Author: Guowei Yang, Yonggui Kao, and Changhong Wang

Source: https://www.hindawi.com/



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