Finite-time stabilization of uncertain neural networks with distributed time-varying delays

Abstract

In this paper, the problem of finite-time stabilization for a class of uncertain neural networks with distributed time-varying delays is investigated. Based on the Lyapunov stability theory and integral inequality technique, some sufficient LMI conditions are derived to ensure the finite-time stability of considered neural networks. In addition, the upper bound of the settling time for stabilization is estimated. Numerical simulations are carried out to demonstrate the effectiveness of the obtained results.

Finite-Time H ∞ State Estimation for Markovian Jump Neural Networks with Time-Varying Delays via an Extended Wirtinger’s Integral Inequality

“…In [29], the authors studied finite-time boundedness for Markovian jump neural networks with L 2 gain analysis. Authors in [26] formulated finite-time stabilization of uncertain neural networks. Exponential state estimation problem has been designed for Markovian jumping neural networks in [18].…”

“…e model consider in this present study is more practical than that proposed by [18,26,29], whereas in this paper, we consider finite-time H ∞ state estimation problem with the combination of Markovian jump neural networks' interval time-varying delay model, which is another advantage. However, the authors in [18,26,29] used some simple techniques in LKFs to solve the stability problems to those articles.…”

“…e model consider in this present study is more practical than that proposed by [18,26,29], whereas in this paper, we consider finite-time H ∞ state estimation problem with the combination of Markovian jump neural networks' interval time-varying delay model, which is another advantage. However, the authors in [18,26,29] used some simple techniques in LKFs to solve the stability problems to those articles. A new LKF with double and triple integral terms and utilizing extended Writnger's integral inequality (EWII) techniques has been proposed for the stochastically finitetime bounded analysis of Markovian jump system in this paper.…”

“…We also studied that finite-time stability involves dynamical systems whose part of the trajectory converges to an equilibrium state in a finite time. Note that the finite-time stability with control frameworks has gained significant attention in recent years [25][26][27][28]. In [29], the authors discussed the finitetime L 2 -gain performance of MJNNs.…”

Passivity of Memristive BAM Neural Networks with Probabilistic and Mixed Time-Varying Delays

“…In 2015, Du et al investigated the passivity of neural networks with discrete and distributed time-varying delays in [44]. In 2016, Yang et al considered finite-time stabilization of uncertain neural networks with distributed time-varying delays in [45]. However, to the best of our knowledge, there are few results on the passivity of MBAMNNs with probabilistic, leakage, and distributed time-varying delays.…”