Stochastic synchronization in an array of neural networks with hybrid nonlinear coupling

2011 ◽  
Vol 74 (18) ◽  
pp. 3808-3815 ◽  
Author(s):  
Jian Feng ◽  
Shenquan Wang ◽  
Zhanshan Wang
Keyword(s):  
Neural Networks ◽  
Nonlinear Coupling ◽  
Stochastic Synchronization

scholarly journals Adaptive stochastic synchronization of delayed reaction–diffusion neural networks

2020 ◽  
Vol 53 (3-4) ◽  
pp. 378-389 ◽  
Author(s):  
Weiyuan Zhang ◽  
Junmin Li ◽  
Jinghan Sun ◽  
Minglai Chen
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Linear Matrix ◽  
Output Coupling ◽  
Delayed Reaction ◽  
Adaptive Feedback ◽  
Adaptive Feedback Control ◽  
Stochastic Synchronization

In this paper, we deal with the adaptive stochastic synchronization for a class of delayed reaction–diffusion neural networks. By combing Lyapunov–Krasovskii functional, drive-response concept, the adaptive feedback control scheme, and linear matrix inequality method, we derive some sufficient conditions in terms of linear matrix inequalities ensuring the stochastic synchronization of the addressed neural networks. The output coupling with delay feedback and the update laws of parameters for adaptive feedback control are proposed, which will be of significance in the real application. The novel Lyapunov–Krasovskii functional to be constructed is more general. The derived results depend on the measure of the space, diffusion effects, and the upper bound of derivative of time-delay. Finally, an illustrated example is presented to show the effectiveness and feasibility of the proposed scheme.

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

2020 ◽  
Vol 25 (6) ◽  
pp. 958-979
Author(s):  
Liangchen Li ◽  
Rui Xu ◽  
Qintao Gan ◽  
Jiazhe Lin
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Physical Property ◽  
Bidirectional Associative Memory ◽  
Stochastic Disturbances ◽  
Stochastic Synchronization ◽  
Analysis Technique ◽  
Switching Controller ◽  
Theoretical Results

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.

scholarly journals Stochastic Synchronization of Neutral-Type Neural Networks with Multidelays Based onM-Matrix

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Wuneng Zhou ◽  
Xueqing Yang ◽  
Jun Yang ◽  
Jun Zhou
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Neutral Type ◽  
Linear Matrix ◽  
Control Law ◽  
Multiple Time ◽  
Matrix Inequality ◽  
Stochastic Disturbance ◽  
Stochastic Synchronization ◽  
Multiple Time Delays

The problem of stochastic synchronization of neutral-type neural networks with multidelays based onM-matrix is researched. Firstly, we designed a control law of stochastic synchronization of the neural-type and multiple time-delays neural network. Secondly, by making use of Lyapunov functional andM-matrix method, we obtained a criterion under which the drive and response neutral-type multiple time-delays neural networks with stochastic disturbance and Markovian switching are stochastic synchronization. The synchronization condition is expressed as linear matrix inequality which can be easily solved by MATLAB. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper.

scholarly journals Generalized Projective Synchronization between Two Different Neural Networks with Mixed Time Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Xuefei Wu ◽  
Chen Xu ◽  
Jianwen Feng ◽  
Yi Zhao ◽  
Xuan Zhou
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Lyapunov Stability Theory ◽  
Projective Synchronization ◽  
Nonlinear Coupling ◽  
Nonlinear Feedback ◽  
Nonlinear Controller ◽  
Feedback Controllers ◽  
Mixed Time Delays ◽  
Generalized Projective Synchronization

The generalized projective synchronization (GPS) between two different neural networks with nonlinear coupling and mixed time delays is considered. Several kinds of nonlinear feedback controllers are designed to achieve GPS between two different such neural networks. Some results for GPS of these neural networks are proved theoretically by using the Lyapunov stability theory and the LaSalle invariance principle. Moreover, by comparison, we determine an optimal nonlinear controller from several ones and provide an adaptive update law for it. Computer simulations are provided to show the effectiveness and feasibility of the proposed methods.

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