Finite-time synchronization of inertial neural networks

Abstract

In this paper, the finite-time synchronization of inertial neural networks is investigated. First, to realize synchronization of the master-slave system, continuous and discontinuous controllers are designed, respectively. By constructing Lyapunov function and using inequalities, some effective criteria are provided to realize synchronization in finite time. Furthermore, in order to achieve synchronization with a fast speed, a new switching controller is presented, and the upper bounds of the settling time of synchronization are estimated. Finally, several numerical simulations are presented to demonstrate the validity of the theoretical results and the effectiveness of the proposed method. Ó 2017 University of Bahrain. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay

“…Remark 4.1. On the one hand, the class of FNTINN with mixed delays have more complex dynamic behaviors compared with traditional NNs [26,28,42,43]. On the other hand, it is delicate to design a Lyapunov functional satisfying the derivative condition for fixed-time stabilization of time-delay systems [32].…”

“…To the best of our knowledge, the stability analysis of neutral-type inertial neural networks with delays has been investigated in [26] and the finite-time synchronization for various kinds of INNs has been obtained in [27,28]. But there is hardly any paper that considered the fixed-time stabilization for FNTINNs with time-varying delay.…”

“…The delay-dependent controller ( 7) with fuzzy terms relies not only on the current state but also on its past state and its derivative. When the past state and its derivative cannot be measured, we must design the delay-free controller (28).…”

Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller

“…Therefore, the investigation of dynamic trajectories is necessary for applied designation of neural networks. Hence, a large number of studies on stability [5][6][7][8], stabilization [9,10], passivity [11], dissipativity [12,13], synchronization [14,15], and state estimation [16,17] for neural networks have been reported.…”

“…On the other hand, many researchers have studied Hopfield neural networks [18], cell neural networks, recurrent neural networks [9,19], Cohen-Grossberg neural networks, bidirectional associative memory neural networks, and Lotka-Volterra neural networks, as well as inertial neural networks [12,14,15,20], which are more intricate than all kinds of prementioned neural networks with the standard resistor-capacitor variety [21]. The inertial term is taken as a critical tool to bring complex bifurcation behavior and chaos.…”