A switching control for finite-time synchronization of memristor-based BAM neural networks with stochastic disturbances

This paper deals with the finite-time stochastic synchronization for a class of memristorbased bidirectional associative memory neural networks (MBAMNNs) with time-varying delays and stochastic disturbances. Firstly, based on the physical property of memristor and the circuit of MBAMNNs, a MBAMNNs model with more reasonable switching conditions is established. Then, based on the theory of Filippov’s solution, by using Lyapunov–Krasovskii functionals and stochastic analysis technique, a sufficient condition is given to ensure the finite-time stochastic synchronization of MBAMNNs with a certain controller. Next, by a further discussion, an errordependent switching controller is given to shorten the stochastic settling time. Finally, numerical simulations are carried out to illustrate the effectiveness of theoretical results.

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New Methods of Finite-Time Synchronization for a Class of Fractional-Order Delayed Neural Networks

Mathematical Problems in Engineering ◽

10.1155/2017/1804383 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Weiwei Zhang ◽

Jinde Cao ◽

Ahmed Alsaedi ◽

Fuad E. Alsaadi

Keyword(s):

Neural Networks ◽

Fractional Order ◽

Finite Time ◽

Time Synchronization ◽

Sufficient Conditions ◽

Time Interval ◽

Finite Time Interval ◽

Delayed Neural Networks ◽

New Methods ◽

Theoretical Results

Finite-time synchronization for a class of fractional-order delayed neural networks with fractional order α, 0<α≤1/2 and 1/2<α<1, is investigated in this paper. Through the use of Hölder inequality, generalized Bernoulli inequality, and inequality skills, two sufficient conditions are considered to ensure synchronization of fractional-order delayed neural networks in a finite-time interval. Numerical example is given to verify the feasibility of the theoretical results.

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New finite-time synchronization analysis of a delayed memristive neurodynamic model

Advances in Difference Equations ◽

10.1186/s13662-020-02929-x ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Dandan Ren ◽

Aidi Yao

Keyword(s):

Neural Networks ◽

Convergence Theorem ◽

Finite Time ◽

Time Synchronization ◽

Switching System ◽

Memristive Neural Networks ◽

Analysis Method ◽

Finite Time Convergence ◽

Inclusion Theory ◽

Theoretical Results

Abstract This paper presents theoretical results on the finite-time synchronization of delayed memristive neural networks (MNNs). Compared with existing ones on finite-time synchronization of discontinuous NNs, we directly regard the MNNs as a switching system, by introducing a novel analysis method, new synchronization criteria are established without employing differential inclusion theory and non-smooth finite time convergence theorem. Finally, we give a numerical example to support the effectiveness of the theoretical results.

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Finite-time synchronization for chaotic neural networks with stochastic disturbances

Advances in Difference Equations ◽

10.1186/s13662-020-03112-y ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Xuejun Shi ◽

Yongshun Zhao ◽

Xiaodi Li

Keyword(s):

Neural Networks ◽

Finite Time ◽

Time Synchronization ◽

Basic Result ◽

Fixed Time ◽

System Response ◽

Stochastic Disturbances ◽

Chaotic Neural Networks ◽

Response System ◽

Error System

AbstractIn this paper, we focus on the problem of synchronization for chaotic neural networks with stochastic disturbances. Firstly, we provide a basic result that the systems including the drive system, response system, and error system have a unique solution on the whole time horizon. Based on this result, we design a new control law such that the response system can be synchronized with the drive chaotic system in finite time. Furthermore, we show that the settling time is independent of the initial data under some proper conditions, which hints that the fixed-time synchronization of chaotic neural networks can be realized by our proposed method. Finally, we give simulations to verify the theoretical analysis for our main results.

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