The Experts below are selected from a list of 1286976 Experts worldwide ranked by ideXlab platform
Fengde Chen - One of the best experts on this subject based on the ideXlab platform.
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almost periodic solution of a discrete commensalism system
Discrete Dynamics in Nature and Society, 2015Co-Authors: Fengde ChenAbstract:A nonautonomous discrete two-species Lotka-Volterra commensalism system with delays is considered in this paper. Based on the discrete comparison theorem, the permanence of the system is obtained. Then, by constructing a new discrete Lyapunov functional, a set of sufficient conditions which guarantee the system global Attractivity are obtained. If the coefficients are almost periodic, there exists an almost periodic solution and the almost periodic solution is globally attractive.
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global Attractivity of an integrodifferential model of mutualism
Abstract and Applied Analysis, 2014Co-Authors: Xiangdong Xie, Fengde Chen, Kun Yang, Yalong XueAbstract:Sufficient conditions are obtained for the global Attractivity of the following integrodifferential model of mutualism: , , where and , , are all positive constants. Consider , Consider and , Our result shows that conditions which ensure the permanence of the system are enough to ensure the global stability of the system. The result not only improves but also complements some existing ones.
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the permanence and global Attractivity of lotka volterra competition system with feedback controls
Nonlinear Analysis-real World Applications, 2006Co-Authors: Fengde ChenAbstract:Abstract In this paper, we consider a nonautonomous Lotka–Volterra system with feedback controls. Some averaged conditions for the permanence and global Attractivity of this system are obtained. Our results generalized those obtained by Zhao et al. (Nonlinear Anal.: Real World Appl. 5 (2004) 265–276).
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existence and global Attractivity of an almost periodic ecological model
Applied Mathematics and Computation, 2004Co-Authors: Yonghui Xia, Fengde Chen, Anping Chen, Jinde CaoAbstract:The almost periodic Lotka-Volterra model with m-predators and n-preys is considered in this paper. By constructing suitable Lyapunov function, some sufficient conditions are obtained for the existence and global Attractivity of a unique positive almost periodic solution of this model. Examples show that our criteria are new, general, and easily verifiable.
Zheng Yan - One of the best experts on this subject based on the ideXlab platform.
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Attractivity analysis of memristor based cellular neural networks with time varying delays
IEEE Transactions on Neural Networks, 2014Co-Authors: Zhenyuan Guo, Jun Wang, Zheng YanAbstract:This paper presents new theoretical results on the invariance and Attractivity of memristor-based cellular neural networks (MCNNs) with time-varying delays. First, sufficient conditions to assure the boundedness and global Attractivity of the networks are derived. Using state-space decomposition and some analytic techniques, it is shown that the number of equilibria located in the saturation regions of the piecewise-linear activation functions of an n-neuron MCNN with time-varying delays increases significantly from 2n to 22n2+n (22n2 times) compared with that without a memristor. In addition, sufficient conditions for the invariance and local or global Attractivity of equilibria or attractive sets in any designated region are derived. Finally, two illustrative examples are given to elaborate the characteristics of the results in detail.
Zhenkun Huang - One of the best experts on this subject based on the ideXlab platform.
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the existence and exponential Attractivity of κ almost periodic sequence solution of discrete time neural networks
Nonlinear Dynamics, 2007Co-Authors: Zhenkun Huang, Xinghua WangAbstract:In the present paper, several sufficient conditions are obtained for the existence and exponential Attractivity of a unique κ-almost periodic sequence solution of discrete time neural network. Our results generalize the corresponding results about almost periodic sequence solution in common sense. It is shown that discretization step κ affects the dynamical characteristics of discrete-time analogues of continuous time neural networks and exponential convergence is dependent on small discretization step size. Our results on exponential Attractivity of κ-almost periodic sequence solution can provide us with relevant estimates on how precise such networks can perform during real-time computations. Finally, computer simulations are performed in the end to show the feasibility of our results.
Jianhua Shen - One of the best experts on this subject based on the ideXlab platform.
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existence and global Attractivity of positive periodic solutions for impulsive predator prey model with dispersion and time delays
Nonlinear Analysis-real World Applications, 2009Co-Authors: Jianhua ShenAbstract:Abstract In this paper, we study the existence and global Attractivity of positive periodic solutions for impulsive predator–prey systems with dispersion and time delays. By using the method of coincidence degree theorem, a set of easily verifiable sufficient conditions are obtained for the existence of at least one strictly positive periodic solution, and by means of a suitable Lyapunov functional, the uniqueness and global Attractivity of the positive periodic solution are presented. Some known results subject to the underlying systems without impulses are improved and generalized.
Xinghua Wang - One of the best experts on this subject based on the ideXlab platform.
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the existence and exponential Attractivity of κ almost periodic sequence solution of discrete time neural networks
Nonlinear Dynamics, 2007Co-Authors: Zhenkun Huang, Xinghua WangAbstract:In the present paper, several sufficient conditions are obtained for the existence and exponential Attractivity of a unique κ-almost periodic sequence solution of discrete time neural network. Our results generalize the corresponding results about almost periodic sequence solution in common sense. It is shown that discretization step κ affects the dynamical characteristics of discrete-time analogues of continuous time neural networks and exponential convergence is dependent on small discretization step size. Our results on exponential Attractivity of κ-almost periodic sequence solution can provide us with relevant estimates on how precise such networks can perform during real-time computations. Finally, computer simulations are performed in the end to show the feasibility of our results.
