Parameter identification based on finite-time synchronization for Cohen–Grossberg neural networks with time-varying delays

• Main Authors: Abdujelil Abdurahman, Haijun Jiang, Cheng Hu, Zhidong Teng
• Format: Article
• Language: English
• Published: Vilnius University Press 2015-07-01
• Series: Nonlinear Analysis
• Subjects: Cohen–Grossberg neural network; Cohen–Grossberg neural network,; finite-time synchronization; parameter identification; time-varying delay
• Online Access: http://www.zurnalai.vu.lt/nonlinear-analysis/article/view/13519

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.