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Your search keyword '"Xu Bingji"' showing total 7 results
Start Over Author "Xu Bingji" Topic lyapunov function
7 results on '"Xu Bingji"'

1. Global Exponential Stability of Delayed Impulsive Hopfield Type Neural Networks

Author

Xu, Bingji, Wang, Qun, Shen, Yi, Liao, Xiaoxin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Liao, Xiaofeng, editor, and Yi, Zhang, editor

Published
2005
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2. Chen 混沌系统全局指数吸引集和正向不变集的构造性证明及应用

Author

Liao Xiao-xin, Yu Pei, XU BingJi, and Chen Guan-Rong

Subjects
S system, Lyapunov function, Discrete mathematics, General Computer Science, Constructive proof, biology, Chaotic, Invariant (physics), biology.organism_classification, Nonlinear Sciences::Chaotic Dynamics, symbols.namesake, Chen, Exponential growth, Chaotic systems, ComputerSystemsOrganization_MISCELLANEOUS, symbols, Applied mathematics, Engineering (miscellaneous), Mathematics
Abstract

In this paper, a constructive proof of the globally exponentially attractive and positively invariant set for the chaotic Chen's system is given, by using several generalized positive de nite Lyapunov functions. The problem of the existence of a globally exponentially attractive set for the chaotic Chen's system is completely solved, which is a long standing open theoretical problem. Methods provided herein can be useful for the study of other forms of chaotic systems.

Published
2015

3. Asymptotic stability of impulsive high-order Hopfield type neural networks

Author

Xu, Bingji, Liu, Xiang, and Teo, Kok Lay

Subjects
*ARTIFICIAL neural networks, *LYAPUNOV functions, *DIFFERENTIAL equations, *ARTIFICIAL intelligence, *LYAPUNOV stability, *STABILITY (Mechanics)
Abstract

Abstract: In this paper, we discuss impulsive high-order Hopfield type neural networks. Investigating their global asymptotic stability, by using Lyapunov function method, sufficient conditions that guarantee global asymptotic stability of networks are given. These criteria can be used to analyse the dynamics of biological neural systems or to design globally stable artificial neural networks. Two numerical examples are given to illustrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Published
2009
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4. Global exponential stability of impulsive high-order Hopfield type neural networks with delays

Author

Xu, Bingji, Liu, Xiang, and Teo, Kok Lay

Subjects
*ARTIFICIAL neural networks, *DIFFERENTIAL inequalities, *DELAY differential equations, *LYAPUNOV functions, *STOCHASTIC convergence
Abstract

Abstract: In this paper, we investigate the global exponential stability of impulsive high-order Hopfield type neural networks with delays. By establishing the impulsive delay differential inequalities and using the Lyapunov method, two sufficient conditions that guarantee global exponential stability of these networks are given, and the exponential convergence rate is also obtained. A numerical example is given to demonstrate the validity of the results. [Copyright &y& Elsevier]

Published
2009
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5. Global exponential stability of high order Hopfield type neural networks

Author

Xu, Bingji, Liu, Xinzhi, and Liao, Xiaoxin

Subjects
*ARTIFICIAL neural networks, *ARTIFICIAL intelligence, *LYAPUNOV functions, *DIFFERENTIAL equations
Abstract

Abstract: This paper studies the problem of global exponential stability for a class of high order Hopfield type neural networks. By utilizing Lyapunov functions, several sufficient conditions for the global exponential stability of equilibrium point are obtained. The exponential convergence rate of the equilibrium point is also estimated. As a special case, several new criteria on global exponential stability for the corresponding first order Hopfield neural networks are obtained. Those criteria generalize some of the recent results reported in the literature. [Copyright &y& Elsevier]

Published
2006
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6. Absolute stability of lurie systems with impulsive effects

Author

Xu, Bingji, Liu, Xinzhi, and Liao, Xiaoxin

Subjects
*LYAPUNOV functions, *DIFFERENTIAL equations, *BESSEL functions, *CALCULUS, *MATHEMATICS
Abstract

This paper studies absolute stability of Lurie systems with impulsive effects. Using the method of Lyapunov functions and the variation of parameters technique, we establish sufficient conditions for absolute stability. [Copyright &y& Elsevier]

Published
2004
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7. LMI-based stability analysis of impulsive high-order Hopfield-type neural networks

Author

Xu, Bingji, Xu, Yuan, and He, Linman

Subjects
*HOPFIELD networks, *STABILITY theory, *ARTIFICIAL neural networks, *LYAPUNOV functions, *LINEAR matrix inequalities, *EQUILIBRIUM
Abstract

Abstract: This paper investigates the asymptotic stability of a class of impulsive high-order neural networks, which can be considered as an expansion of Hopfield neural networks. By employing Lyapunov functions and linear matrix inequality (LMI) technique, sufficient conditions that guarantee the global asymptotic stability of the equilibrium point are derived. The proposed criteria are easily verified and possess many adjustable parameters, which provide flexibility for the analysis of the neural networks. Finally, two examples are given to show the effectiveness of the proposed results. [Copyright &y& Elsevier]

Published
2012
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