Parameter identification based on finite-time synchronization for Cohen–Grossberg neural networks with time-varying delays
Abdujelil Abdurahman, Haijun Jiang, Cheng Hu, Zhidong Teng · Nonlinear Analysis Modelling and Control · 2015
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.