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Stability of stochastic neural networks with Markovian jumping parameters

Journal of Systems Engineering and Electronics 2009年第3期20卷 613-618页

作者： Hua Mingang Deng Feiqi Peng Yunjian Coll. of Automation Science and Engineering South China Univ. of Technology Guangzhou 510640 P. R. China

The global asymptotical stability for a class of stochastic delayed neural networks （SDNNs） with Maxkovian jumping parameters is considered. By applying Lyapunov functional method and Ito＇s differential rule, new delay-dependent stability conditions are derived. All results are expressed in terms of linear matrix inequality （LMI）, and a numerical example is presented to illustrate the correctness and less conservativeness of the proposed method.

关键词： stochastic neural networks global asymptotical stability linear matrix inequality Markovian jumping parameters.

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Delay-probability-distribution-dependent robust stability analysis for stochastic neural networks with time-varying delay

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Progress in Natural Science:Materials International 2009年第10期19卷 1333-1340页

作者： Jie Fu,Huaguang Zhang,Tiedong Ma School of Information Science and Engineering,Northeastern University,Box 134,Shenyang 110004,China School of Information Science and Engineering Northeastern University Box 134 Shenyang 110004 China

The delay-probability-distribution-dependent robust stability problem for a class of uncertain stochastic neural networks(SNNs) with time-varying delay is investigated.The information of probability distribution of the time delay is considered and transformed into parameter matrices of the transferred SNNs model.Based on the Lyapunov-Krasovskii functional and stochastic analysis approach,a delay-probability-distribution-dependent sufficient condition is obtained in the linear matrix inequality(LMI) format such that delayed SNNs are robustly globally asymptotically stable in the mean-square sense for all admissible uncertainties.An important feature of the results is that the stability conditions are dependent on the probability distribution of the delay and upper bound of the delay derivative,and the upper bound is allowed to be greater than or equal to 1.Finally,numerical examples are given to illustrate the effectiveness and less conservativeness of the proposed method.

关键词： Delay-probability-distribution-dependent stochastic neural networks Time-varying delay Linear matrix inequality

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Novel delay-probability-distribution-dependent mean square stability analysis for stochastic neural networks

Novel delay-probability-distribution-dependent mean square s...

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第36届中国控制会议

作者： LUO Jinnan TIAN Wenhong ZHONG Shouming SHI Kaibo WANG Wenqin School of Information and Software Engineering University of Electronic Science and Technology of China School of Mathematical Sciences University of Electronic Science and Technology of China School of Information Science and Engineering Chengdu University School of Sciences Tianjin Polytechnic University

ISBN: (纸本)9781538629185

In this paper, the delay-probability-distribution-dependent stability problem for stochastic neural networks with various time-varying delays is investigated. By considering a new Lyapunov-Krasovskii functional（LKF） concluding more delaypartitioning term and combining free weight matrices and stochastic processing techniques, an improved delay-probabilitydistribution-dependent condition is built in terms of linear matrix inequality（LMI） so that the system is mean square stable.Finally, a numerical example is given to verify the effectiveness the proposed criterion.

关键词： stochastic neural networks Probability-distribution Multiple delays

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New Delay Segment-Dependent Asymptotic Stability for stochastic neural networks With Interval Time-Varying Delay and Distributed Delays

New Delay Segment-Dependent Asymptotic Stability for Stochas...

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2011 Chinese Control and Decision Conference（CCDC）

作者： Jianwei Xia is with School of Automation,Southeast University,Nanjing,Jiangsu,China,and School of Mathematics Science,Liaocheng University,Liaocheng,Shangdong,China.Xianping Chen is with School of Mathematics Science,Liaocheng University,Liaocheng,Shandong,China.Yonggang Chen is with Department of Mathematics,Henan Institute of Science and Technology,Xinxiang,Henan,China,Yongmin Li is with Department of Mathematics,School of Science,Huzhou Teachers College,Huzhou,and School of Automation,Southeast University,Nanjing,Jiangsu,China

This paper deals with the problem of stability for a class of stochastic neural networks with interval timevarying delays and distributed delay.Based on the idea of nonuniform partitioning the delay interval,new delay-interval dependent stability are proposed by constructing a novel Augmented Lyapunov-Krasovskii functional in terms of linear matrix inequalities（LMIs）.

关键词： stochastic neural networks nonuniform delay partitioning stability interval time-varying delays

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A State Estimator of stochastic Delayed neural networks

A State Estimator of Stochastic Delayed Neural Networks

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第24届中国控制与决策会议

作者： Chunxiao Zhang 1 , Yun Chen 1 , Junhong Wang 1 1. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

The problem of state estimation for stochastic Hopfield neural networks with time-varying delay is investi- gated in this paper. Based on an auxiliary vector and free-weighting matrix technique, a delay-dependent Luenberger- type state estimator, which ensures mean-square asymptotic stability of the resulting filtering error state system, is de- signed. In this paper, the model transformations and cross terms bounding techniques are avoided. A numerical example is proposed to show the validity of the method.

关键词： stochastic neural networks time-varying delay auxiliary vector Lyapunov-Krasovskii functional

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Further results on mean square exponential stability of uncertain stochastic delayed neural networks

Further results on mean square exponential stability of unce...

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2010 Chinese Control and Decision Conference

作者： Yuanyuan Wu~1,Yuqiang Wu~2 1.School of Automation,Southeast University,Nanjing 210096 2.Research Institute of Automation,Qufu Normal University,Qufu 273165,China

＜正＞This letter is concerned with the mean square exponential stability problem for a class of uncertain stochastic neural networks with time-varying delay.The activation functions are assumed to be neither monotonic,nor differentiable. The proposed delay-dependent stability criterion is derived by utilizing the constructed Lyapunov-Krasovskii functional and the novel technique.A numerical example is presented to show the effectiveness and improvement of the proposed method.

关键词： stochastic neural networks Time-varying delay Mean square exponential stability Linear matrix inequalities

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Delay-dependent Robust Stability for Uncertain stochastic neural networks with Multiple Mixed Time-varying Delays

Delay-dependent Robust Stability for Uncertain Stochastic Ne...

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第二十九届中国控制会议

作者： QIN Tiheng1,PAN Quanxiang2,CHEN Yonggang2 1.Department of Basic Courses,Henan Mechanical and Electrical Engineering College,Xinxiang 453002,P.R.China2.Department of Mathematics,Henan Institute of Science and Technology,Xinxiang 453003,P.R.China

In this paper,the stability problem is considered for uncertain stochastic neural networks with multiple discrete and distributed time-varying delays.By choosing the suitable Lyapunov-Krasovskii functional,and combining with Jensen integral inequality,two delay-dependent stability and robust stability conditions are proposed in terms of linear matrix inequalities（LMIs）.The obtained results generalize and improve some existing ones in the literatures.

关键词： Robust Stability stochastic neural networks Multiple Time-varying Delays Linear Matrix Inequalities（LMIs）

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Mean Square Stability for stochastic neural networks with Distributed and Interval Time-varying Delays

Mean Square Stability for Stochastic Neural Networks with Di...

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2009中国控制与决策会议

作者： Haixia Wu~(1,2),Wei Feng~1,Wei Zhang~1,Songjian Dan~2 1.Department of Computer and Modern Education Technology,Chongqing Education College,Chongqing 400067,China 2.Department of Further Education,Chongqing Education College,Chongqing 400067,China

＜正＞This paper is concerned with the asymptotic stability analysis problem for stochastic neural networks with distributed and interval time-varying delays.By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which takes into account the ranges of delays,a new delay-range-dependent stability criterion is established to guarantee the delayed neural networks to be asymptotically stable in the mean square.A numerical example has also been used to demonstrate the usefulness of the main result.

关键词： Stability stochastic neural networks Distributed delays Interval time-varying delays Linear matrix inequalities（LMIs）

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New Delay-Dependent Robust Stability Criteria for Uncertain stochastic neural networks with Time-Varying Delays

New Delay-Dependent Robust Stability Criteria for Uncertain ...

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第二十七届中国控制会议

作者： Feng Wei 1,2,Zhang Wei 2,Wu Haixia 2,Huang Song 2 1.College of Automation,Chongqing University,Chongqing 400030,P.R.China 2.Department of Computer and Modern Education Technology,Chongqing Education College,Chongqing 400067,P.R.China

The problem of stochastic robust stability of a class of stochastic neural networks with time-varying delays and parameter uncertainties is investigated in this paper.The parameter uncertainties are time-varying and norm-bounded.The time-delay factors are unknown and time-varying with known bounds.Based on Lyapunov-Krasovskii functional and stochastic analysis approaches,some new stability criteria are presented in terms of linear matrix inequalities（LMIs） to guarantee the delayed neural network to be robustly stochastically asymptotically stable in the mean square for all admissible uncertainties.Numerical examples are given to demonstrate the usefulness of the proposed robust stability criteria.

关键词： stochastic neural networks Robust stability Linear Matrix Inequality （LMIs） Uncertainties

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Robust exponential stability of uncertain discrete-time stochastic neural networks with impulses

Robust exponential stability of uncertain discrete-time stoc...

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第32届中国控制与决策会议

作者： Ting Cai Pei Cheng Zheng Wu School of Mathematical Sciences Anhui University

This paper focuses on the problem of robust exponential stability of discrete-time impulsive neural networks with parametric uncertainties.By Lyapunov function and stochastic analysis approaches,some robust exponential stability criteria are given.Three types of impulses are considered:stabilizing impulses,destabilizing impulses and neutral impulses.The obtained results have shown that discrete-time neural networks with delays can be robustly exponentially stabilized by an appropriate impulse sequences.Finally,a numerical example is given to show the effectiveness of the results.

关键词： stochastic neural networks Impulses Exponential stability Linear matrix inequalities

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