Finite-Time Stabilization of Competitive Neural Networks With Time-Varying Delays

2021 ◽  
pp. 1-10
Yin Sheng ◽  
Zhigang Zeng ◽  
Tingwen Huang
2021 ◽  
Vol 26 (5) ◽  
pp. 759-780
Fanchao Kong ◽  
Quanxin Zhu ◽  
Rathinasamy Sakthivel

This article aims to study a class of discontinuous fuzzy inertial Cohen–Grossberg neural networks (DFICGNNs) with discrete and distributed time-delays. First of all, in order to deal with the discontinuities by the differential inclusion theory, based on a generalized variable transformation including two tunable variables, the mixed time-varying delayed DFICGNN is transformed into a first-order differential system. Then, by constructing a modified Lyapunov–Krasovskii functional concerning with the mixed time-varying delays and designing a delayed feedback control law, delay-dependent criteria formulated by algebraic inequalities are derived for guaranteeing the finite-time stabilization (FTS) for the addressed system. Moreover, the settling time is estimated. Some related stability results on inertial neural networks is extended. Finally, two numerical examples are carried out to verify the effectiveness of the established results.

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15
Ge Li ◽  
Yaqiong Li ◽  
Zhaohui Yuan

In this paper, the finite-time stabilization problem for memristive Cohen-Grossberg neural networks with time-varying delay is discussed. By using the novel fixed point theory of set-valued maps, we establish the existence theorem of equilibrium point. In order to realize the finite-time stabilization, two different kinds of discontinuous state feedback controllers whether including time-varying delay are designed. Based on the extended Filippov framework and two different kinds of methods whether using finite-time stability theory, some novel sufficient conditions and the upper bound of the settling time for finite-time stabilization are proposed. Finally, two numerical examples are given to demonstrate the validity of theoretical results.

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