Technical Notes and Correspondence

International Journal of Control, Automation and Systems 2011; 9(1): 187-196

Published online February 12, 2011

https://doi.org/10.1007/s12555-011-0124-4

© The International Journal of Control, Automation, and Systems

Exponential Synchronization for Arrays of Coupled Neural Networks with Time-delay Couplings

Tao Li, Ting Wang, Ai-guo Song, and Shu-min Fei

Henan Polytechnic University, China

Abstract

This paper deals with global exponential synchronization in arrays of coupled delayed neural networks with both delayed coupling and one single delayed one. Through employing Kronecker product and convex combination technique, two novel synchronization criteria are presented in terms of linear ma-trix inequalities (LMIs), and these conditions are dependent on the bounds of both time-delay and its derivative. Through employing Matlab LMI Toolbox and adjusting some matrix parameters in the derived results, we can realize the design and applications of the addressed systems, which shows that our methods improve and extend those reported methods. The efficiency and applicability of the proposed results can be demonstrated by three numerical examples with simulations.

Keywords Coupled neural networks, exponential synchronization, LMI approach, Lyapunov-Krasovskii functional, time-varying delay.

Article

Technical Notes and Correspondence

International Journal of Control, Automation and Systems 2011; 9(1): 187-196

Published online February 1, 2011 https://doi.org/10.1007/s12555-011-0124-4

Copyright © The International Journal of Control, Automation, and Systems.

Exponential Synchronization for Arrays of Coupled Neural Networks with Time-delay Couplings

Tao Li, Ting Wang, Ai-guo Song, and Shu-min Fei

Henan Polytechnic University, China

Abstract

This paper deals with global exponential synchronization in arrays of coupled delayed neural networks with both delayed coupling and one single delayed one. Through employing Kronecker product and convex combination technique, two novel synchronization criteria are presented in terms of linear ma-trix inequalities (LMIs), and these conditions are dependent on the bounds of both time-delay and its derivative. Through employing Matlab LMI Toolbox and adjusting some matrix parameters in the derived results, we can realize the design and applications of the addressed systems, which shows that our methods improve and extend those reported methods. The efficiency and applicability of the proposed results can be demonstrated by three numerical examples with simulations.

Keywords: Coupled neural networks, exponential synchronization, LMI approach, Lyapunov-Krasovskii functional, time-varying delay.

IJCAS
November 2024

Vol. 22, No. 11, pp. 3253~3544

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