Letters: Exponential synchronization for delayed chaotic neural networks with nonlinear hybrid coupling

Authors:
Guobao Zhang;Ting Wang;Tao Li;Shumin Fei
Affiliations:
Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing 210096, China;Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing 210096, China;School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing 210096, China
Venue:
Neurocomputing
Year:
2012

Citing 15
Cited 3

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Global Synchronization in an Array of Delayed Neural Networks With Hybrid Coupling

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Exponential synchronization of chaotic neural networks with mixed delays and impulsive effects via output coupling with delay feedback

Mathematical and Computer Modelling: An International Journal

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Abstract

This paper deals with global exponential synchronization in arrays of coupled delayed chaotic neural networks with nonlinear hybrid coupling. Through constructing one novel Lyapunov-Krasovskii functional, two novel synchronization criteria are presented in terms of linear matrix inequalities (LMIs) based on reciprocal convex technique, and these conditions are heavily dependent on the bounds of both time-delay and its derivative. Through employing LMI in Matlab Toolbox and adjusting some matrix parameters in the derived results, the design and applications of the generalized networks can be realized, which shows that our methods can improve some reported methods. The efficiency and applicability of the proposed methods can be demonstrated by three numerical examples with simulations.