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robust exponential stability of a Class of Fractional Order Hopfield Neural Networks

Journal of Mathematical Research with Applications 2015年第6期35卷 653-658页

作者： Xiaolei LIU Mingjiu GAI Shiwei CUI Institute of System Science and Mathematics Naval Aeronautical and Astronautical University

In this paper, we investigate the robust exponential stability of a class of fractional order Hopfield neural network with Caputo derivative, and we get some sufficient conditions to guarantee its robust exponential stability. Finally, we use one numerical simulation example to illustrate the correctness and effectiveness of our results.

关键词： fractional order neural networks Gronwall inequality robust exponential stability

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New robust exponential stability Analysis for Uncertain Neural Networks with Time-varying Delay

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International Journal of Automation and computing 2008年第4期5卷 395-400页

作者： Yong-Gang Chen Wei-Ping Bi Department of Mathematics Henan Institute of Science and TechnologyXinxiang 453003PRC College of Mathematics and Information Science Henan Normal UniversityXinxiang 453007PRC

In this paper,the global robust exponential stability is considered for a class of neural networks with parametric uncer- tainties and time-varying *** using Lyapunov functional method,and by resorting to the new technique for estimating the upper bound of the derivative of the Lyapunov functional,some less conservative exponential stability criteria are derived in terms of linear matrix inequalities (LMIs).Numerical examples are presented to show the effectiveness of the proposed method.

关键词： robust exponential stability uncertain neural networks time-varying delay Lyapunov functional method linear matrix inequalities (LMIs).

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robust exponential stability of intervl neural networks with delays

Robust exponential stability of intervl neural networks with...

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第二十一届中国控制会议

作者： SUN Chang-yin, FENG Chun-bo (Research Institute of Automation, Southeast University, Nanjing 210096)

＜正＞In this paper, based on globally Lipschitz continous activation functions, new conditions ensuring existence, uniqueness and global robust exponential stability of the equilibrium point of interval-delayed neural networks are obtained. The delayed Hopfield neural network, bidirectional associative memory neural network and cellular neural network are special cases of the network model considered in this paper. All the results obtained are generalizations of some resent results reported in the literature for neural networks with constant delays.

关键词： robust exponential stability interval neural networks delays

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Resilient control of flexible hypersonic vehicles against unmatched distributed faults

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Chinese Journal of Aeronautics 2024年第5期37卷 60-70页

作者： Dong ZHAO Wenjing REN School of Artificial Intelligence Anhui UniversityHefei 230601China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China School of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009China

This paper studies a robust fault compensation and vibration suppression problem of flexible hypersonic *** controlled plant is represented by a cascade system composed of a nonlinear Ordinary Differential Equation(ODE)and an Euler-Bernoulli Beam Equation(EBBE),in which the vibration dynamics is coupled with the rigid dynamics and suffers from distributed faults.A state differential transformation is introduced to transfer distributed faults to an EBBE boundary and a longitudinal dynamics is refined by utilizing T-S fuzzy IF-THEN rules.A novel T-S fuzzy based fault-tolerant control algorithm is developed and related stability conditions are *** robust exponential stability and well-posedness are proved by using the modified l_(0)-semigroup based Lyapunov direct approach.A simulation study on the longitudinal dynamics of flexible hypersonic vehicles effectively verifies the validity of the developed theoretical results.

关键词： Flexible hypersonic vehicles ODE-EBBE cascade robust exponential stability Unmatched distributed fault compensation Vibration suppression

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Augmented Lyapunov Approach to exponential stability of Discrete-Time Neural Networks

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Journal of Mathematical Research and Exposition 2011年第3期31卷 479-489页

作者： Zi Xin LIU Shu LU Shou Ming ZHONG Mao YE School of Mathematics and Statistics Guizhou College of Finance and Economics Guizhou 550004 P. R. China School of Mathematical Sciences University of Electronic Science and Technology of China Sichuan 611731 P. R. China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan 611731 P. R. China

This paper addresses the problem of robust stability for a class of discrete-time neural networks with time-varying delay and parameter *** constructing a new augmented Lyapunov-Krasovskii function,some new improved stability criteria are obtained in forms of linear matrix inequality（LMI） *** with some recent results in the literature,the conservatism of these new criteria is reduced *** numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.

关键词： discrete-time neural networks robust exponential stability delay-dependent criterion time-varying delay.

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