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Improved stochastic synchronization criterion of delayed Markovian coupled neural networks with random coupling strengths and partial information on transition probabilities 36

Improved stochastic synchronization criterion of delayed Mar...

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第36届中国控制会议

作者： Deqiang Zeng Ruimei Zhang Kaibo Shi Shouming Zhong Data Recovery Key Laboratory of Sichuan Province College of Mathematics and Information ScienceNeijiang Normal University School of Mathematics Sciences University of Electronic Science and Technology of China School of Information Sciences and Engineering Chengdu University

ISBN: (纸本)9781538629185

This paper investigates the synchronization problem of delayed Markovian coupled neural networks with random coupling strengths and partial information on transition probabilities. In this coupled neural network model, each transition rate is only known partial information, and the coupling strengths are characterized by mutually independent random *** constructing a new augmented Lyapunov-Krasovskii functional（LKF） and reciprocal convex combination, synchronization criterion is derived. In comparison with existing results, the established criterion is not only less conservative but with less computational burden. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed criterion.

关键词： Markovian coupled neural networks stochastic synchronization Random coupling strengths Partial information on transition probabilities

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stochastic synchronization of coupled harmonic oscillators in the sense of probability distribution

Stochastic synchronization of coupled harmonic oscillators i...

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第十届动力学与控制学术会议

作者： WANG Guo-qiang ZHOU Jin Shanghai Institute of Applied Mathematics and Mechanics and Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai University

In this paper, the issue of stochastic synchronization of coupled harmonic oscillators in the sense of probability distribution is studied. Based on stochastic dynamical system theory, we show that the distribution of the synchronization solution of coupled harmonic oscillator canexponentially converge to an invariant measure. Moreover, some sufficient conditions of stochastic synchronization for such coupled harmonic oscillators are established. To this end, numerical simulations are presented to further demonstrate the effectiveness of the proposed approach.

关键词： stochastic synchronization coupled harmonic oscillator invariant measure

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Effect of spatially correlated noise on stochastic synchronization in globally coupled FitzHugh–Nagumo neuron systems

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Theoretical & Applied Mechanics Letters 2014年第1期4卷 41-49页

作者： Yange Shao Yanmei Kang Department of Applied Mathematics School of Mathematics and Statistics Xi'an Jiaotong University

The phenomenon of stochastic synchronization in globally coupled FitzHugh–Nagumo（FHN） neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation（DMA） and direct simulation（DS）. Results from DMA are in good quantitative or qualitative agreement with those from DS for weak noise intensity and larger system size. Whether the consisting single FHN neuron is staying at the resting state, subthreshold oscillatory regime, or the spiking state, our investigation shows that the synchronization ratio of the globally coupled system becomes higher as the noise correlation coefficient increases, and thus we conclude that spatial correlation has an active effect on stochastic synchronization, and the neurons can achieve complete synchronization in the sense of statistics when the noise correlation coefficient tends to one. Our investigation also discloses that the noise spatial correlation plays the same beneficial role as the global coupling strength in enhancing stochastic synchronization in the ensemble. The result might be useful in understanding the information coding mechanism in neural systems.

关键词： stochastic synchronization spatially correlated noise dynamical mean-field approximation

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synchronization of Delayed Chaotic Neural Networks with stochastic Disturbances via Periodically Intermittent Control

Synchronization of Delayed Chaotic Neural Networks with Stoc...

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2011 Chinese Control and Decision Conference（CCDC）

作者： Yan Chen,Guiyin Shi and Hongji Tang are with School of Science,Nantong University,Nantong 226007,China Xiaomei Zhang,is with School of Electronics and Information,Nantong University,Nantong 226019,China

This paper deals with the synchronization problem of delayed chaotic neural networks with stochastic disturbances using periodically intermittent *** stability theory and free-weighting matrix method are utilized to establish stochastic synchronization criteria for designing intermittent control law,which guarantees that the synchronization error system is exponentially stable in the mean *** the assumption that the control period and control width are fixed and known,the desired intermittent controller is designed in terms of linear matrix inequalities.A numerical example is illustrated to demonstrate the effectiveness of the proposed method.

关键词： Chaotic neural networks stochastic synchronization intermittent control time delay mean square exponential stability

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Semi-global finite-time synchronization of stochastic complex networks

Semi-global finite-time synchronization of stochastic comple...

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第40届中国控制会议

作者： Liangliang Li Wenlin Jiang School of Mathematical Sciences Huaibei Normal University

This paper presents the problem of finite-time synchronization for stochastic complex networks. By establishing a novel finite-time L-operator differential inequality, combining with inequality techniques, some novel sufficient conditions are obtained to ensure finite-time stochastic synchronization for the complex networks concerned. A novel control method is given to guarantee inner finite-time synchronization of the system, and the maximum time of convergence is estimated. Finally, one example with numerical simulations is given to demonstrate the effectiveness of our results.

关键词： stochastic complex network Finite-time synchronization stochastic synchronization L-operator differential inequality Chaotic networks

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