Parameter identification based on finite-time synchronization for Cohen–Grossberg neural networks with time-varying delays
Articles
Abdujelil Abdurahman
Xinjiang University, China
Haijun Jiang
Xinjiang University, China
Cheng Hu
Xinjiang University, China
Zhidong Teng
Xinjiang University, China
Published 2015-07-20
https://doi.org/10.15388/NA.2015.3.3
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Keywords

Cohen–Grossberg neural network
Cohen–Grossberg neural network,
finite-time synchronization
parameter identification
time-varying delay

How to Cite

Abdurahman A., Jiang H., Hu C. and Teng Z. (2015) “Parameter identification based on finite-time synchronization for Cohen–Grossberg neural networks with time-varying delays”, Nonlinear Analysis: Modelling and Control, 20(3), pp. 348-366. doi: 10.15388/NA.2015.3.3.

Abstract

In this paper, the finite-time synchronization problem for chaotic Cohen–Grossberg neural networks with unknown parameters and time-varying delays is investigated by using finite-time stability theory. Firstly, based on the parameter identification of uncertain delayed neural networks, a simple and effective feedback control scheme is proposed to tackle the unknown parameters of the addressed network. Secondly, by modifying the error dynamical system and using some inequality techniques, some novel and useful criteria for the finite-time synchronization of such a system are obtained. Finally, an example with numerical simulations is given to show the feasibility and effectiveness of the developed methods.

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