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Adaptive synchronization in an array of asymmetric coupled neural networks

Chinese Physics B 2009年第1期18卷 76-83页

作者：高明崔宝同 College of Communication and Control Engineering Jiangnan University

This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated laws, some sufficient conditions are derived for global synchronization of the coupled neural networks. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the realistic network. It is shown that the approaches developed here extend and improve the earlier works. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

关键词： coupled neural networks adaptive synchronization Lyapunov functional delayed coupling

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ADAPTIVE PINNING SYNCHRONIZATION OF coupled neural networks WITH MIXED DELAYS AND VECTOR-FORM STOCHASTIC PERTURBATIONS

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Acta Mathematica Scientia 2012年第3期32卷 955-977页

作者：杨鑫松曹进德 Department of Mathematics Chongqing Normal University Department of Mathematics Southeast University

In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also considered. On the basis of two sim- ple adaptive pinning feedback control schemes, Lyapunov functional method, and stochas- tic analysis approach, several sufficient conditions are developed to guarantee global syn- chronization of the coupled neural networks with two kinds of delay couplings, even if only partial states of the nodes are coupled. The outer-coupling matrices may be symmetric or asymmetric. Unlike existing results that an isolate node is introduced as the pinning target, we pin to help the network realizing synchronization without introducing any iso- late node when the network is not synchronized. As a by product, sufficient conditions under which the network realizes synchronization without control are derived. Numerical simulations confirm the effectiveness of the obtained results.

关键词： coupled neural networks mixed delays synchronization vector-form noises pinning adaptive asymmetric coupling

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Global synchronization of discrete-time coupled neural networks with Markovian switching and impulses

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International Journal of Biomathematics 2016年第3期9卷 157-176页

作者： Jie Tan Chuandong Li College of Electronic and Information EngineeringSouthwest University Chongqing 400715 P. R. China College of Mathematics and PhysicsChongqing University of Science and Technology Chongqing 401331 P. R. China

This paper is concerned with the problem of synchronization anMysis for discrete-time coupled neural networks. The networks under consideration are subject to：（1） the jump- ing parameters that are modeled as a continuous-time, discrete-state Markov process; （2） impulsive disturbances; and （3） time delays that include both the mode-dependent discrete and distributed delay. By constructing suitable Lyapuno-Krasovskii functional and combining with linear matrix inequality approach, several novel criteria are derived for verifying the global exponential synchronization in the mean square of such stochas- tic dynamical networks. The derived conditions are established in terms of linear matrix inequalities, which can be easily solved by some available software packages. A simu- lation example is presented to show the effectiveness and applicability of the obtained results,

关键词： Global synchronization coupled neural networks Markovian switching impulses.

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Exponential synchronization of coupled memristive neural networks via pinning control

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Chinese Physics B 2013年第5期22卷 203-212页

作者：王冠沈轶尹泉 Department of Control Science and Engineering Huazhong University of Science and Technology Key Laboratory of Ministry of Education for Image Processing and Intelligent Control

This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled sub- systems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.

关键词： synchronization memristor coupled neural networks pinning control

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Topology Identification of Multiple Output coupled neural networks with and without Parameter Uncertainties

Topology Identification of Multiple Output Coupled Neural Ne...

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第40届中国控制会议

作者： Xiao-Lu Liu Han-Yu Wu Shun-Yan Ren Yan-Li Huang School of Computer Science and Technology Tiangong University School of Mechanical Engineering Tiangong University

This paper concentrates on the research of the topology identification problem for coupled neural networks with multiple output couplings（CNNMOCs）. Based on the designed adaptive state feedback controller and coupling weight adjustment scheme, a topology identification criterion is presented. Furthermore, the case that the parameter uncertainties occur in individual neural network is also discussed. Lastly, two numerical examples are provided to demonstrate the validity of the proposed criteria.

关键词： coupled neural networks Multiple Output Couplings Parameter Uncertainties Synchronization Topology Identification

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Exponential stochastic synchronization of coupled neural networks with adaptive intermittent control

Exponential stochastic synchronization of coupled neural net...

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第36届中国控制会议

作者： Haiyi Sun Ning Li Zhongtang Chen Shuxian Miao College of Science Shenyang Jian Zhu University College of Sciences Northeastern University

ISBN: (纸本)9781538629185

In this paper, the exponential stochastic synchronization of coupled neural networks via adaptive periodically intermittent control is further investigated. Based on the Lyapunov stability theory combined with the method of the adaptive control,periodically intermittent control and the properties of Weiner process, some simple criteria are derived for the exponential stochastic synchronization of the coupled neural networks with coupling delays under stochastic perturbations. The adaptive periodically intermittent control which we have obtained can cut down control cost. The sufficient conditions of this paper for network synchronization are less conservative and can be applied in a wider area. The numerical example of the stochastically coupled neural networks is given to demonstrate the effectiveness of the control strategies.

关键词： coupled neural networks Chaotic delayed neural networks Exponential stochastic synchronization Adaptive periodically intermittent control

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