Finite-time stabilization of memristor-based inertial neural networks with time-varying delays combined with interval matrix method

2021 ◽  
pp. 107395
Author(s):  
Fei Wei ◽  
Guici Chen ◽  
Wenbo Wang
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Matrix Method ◽  
Interval Matrix ◽  
Time Varying ◽  
Inertial Neural Networks ◽  
Finite Time Stabilization

scholarly journals Finite-time stabilization of discontinuous fuzzy inertial Cohen–Grossberg neural networks with mixed time-varying delays

2021 ◽  
Vol 26 (5) ◽  
pp. 759-780
Author(s):  
Fanchao Kong ◽  
Quanxin Zhu ◽  
Rathinasamy Sakthivel
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Delayed Feedback Control ◽  
Control Law ◽  
Time Varying ◽  
Inertial Neural Networks ◽  
Inclusion Theory ◽  
Finite Time Stabilization ◽  
Delay Dependent ◽  
Stability Results

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.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Deyi Li ◽  
Yuanyuan Wang ◽  
Guici Chen ◽  
Shasha Zhu
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite Time ◽  
Design Method ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Nonlinear Feedback ◽  
Inertial Neural Networks ◽  
Finite Time Stabilization

This paper pays close attention to the problem of finite-time stabilization related to stochastic inertial neural networks with or without time-delay. By establishing proper Lyapunov-Krasovskii functional and making use of matrix inequalities, some sufficient conditions on finite-time stabilization are obtained and the stochastic settling-time function is also estimated. Furthermore, in order to achieve the finite-time stabilization, both delayed and nondelayed nonlinear feedback controllers are designed, respectively, in terms of solutions to a set of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate the correction of the theoretical results and the effectiveness of the proposed control design method.

Finite-time synchronization of memristor neural networks via interval matrix method

2020 ◽  
Vol 127 ◽  
pp. 7-18 ◽  
Author(s):  
Fei Wei ◽  
Guici Chen ◽  
Wenbo Wang
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Matrix Method ◽  
Time Synchronization ◽  
Interval Matrix
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