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

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 finitetime 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.

Stability analysis for delayed quaternion-valued neural networks via nonlinear measure approach

“…T . Therefore, one can investigate the existence and uniqueness of the equilibrium of system (2) instead of system (1).…”

“…Real-valued neural networks (RVNNs) have been successfully applied in secure communication, information processing, engineer optimization, automatic control engineering, and other areas. Correspondingly, numerous meaningful results have been reported [1,4,5,9,10,29,31,36,[45][46][47]. In order to ensure the fixed-time synchronization for memristive neural networks, Cao and Li proposed some control strategies to achieve desired performance in [9].…”

Synchronization in nonlinear complex networks with multiple time‐varying delays via adaptive aperiodically intermittent control

“…[15][16][17] In most of the existing works on synchronization of complex networks, one assumption adopted is that the inner couplings are linear such as the references [18][19][20] and linear coupling considers the state variables as a priori knowledge. [15][16][17] In most of the existing works on synchronization of complex networks, one assumption adopted is that the inner couplings are linear such as the references [18][19][20] and linear coupling considers the state variables as a priori knowledge.…”

“…Synchronization, as a typical collective behavior in natural phenomena, has attracted extensive focus among researchers in recent years because of its potential applications in many fields such as the parameter identification, secure communication, and information processing. [15][16][17] In most of the existing works on synchronization of complex networks, one assumption adopted is that the inner couplings are linear such as the references [18][19][20] and linear coupling considers the state variables as a priori knowledge. However, the state variables cannot be observed directly in practice.…”

Finite-time synchronization of Markovian neural networks with proportional delays and discontinuous activations

“…Actually, although different types of synchronization have been considered in the literature, they can be classified into two kinds according to the time of achieving synchronization: (i) infinite-time synchronization (including asymptotic and exponential synchronization) [5,12,15,23,32,56]; (ii) finite-time synchronization [1,3,22,34,38,[42][43][44]51,54]. Finite-time synchronization means that synchronization can be realized in a finite settling time, which is more practical than infinite-time synchronization since the life spans of human beings and machines are limit.…”