The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Kelin Li - One of the best experts on this subject based on the ideXlab platform.
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delay dependent exponential stability for impulsive cohen grossberg neural networks with time varying delays and reaction diffusion terms
Communications in Nonlinear Science and Numerical Simulation, 2011Co-Authors: Xinhua Zhang, Shulin Wu, Kelin LiAbstract:Abstract In this paper, a class of impulsive Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion is formulated and investigated. By employing delay Differential Inequality and the linear matrix Inequality (LMI) optimization approach, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive Cohen–Grossberg neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of Cohen–Grossberg neural networks. An example is given to show the effectiveness of the results obtained here.
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stability analysis for impulsive cohen grossberg neural networks with time varying delays and distributed delays
Nonlinear Analysis-real World Applications, 2009Co-Authors: Kelin LiAbstract:In this paper, a class of impulsive Cohen–Grossberg neural networks with time-varying delays and distributed delays is investigated. By establishing an integro-Differential Inequality with impulsive initial conditions, employing the M-matrix theory and the nonlinear measure approach, some new sufficient conditions ensuring the existence, uniqueness, global exponential stability and global robust exponential stability of equilibrium point for impulsive Cohen–Grossberg neural networks with time-varying delays and distributed delays are obtained. In particular, a more precise estimate of exponential convergence rate is provided. By comparisons and examples, it is shown that the results obtained here can extremely extend and improve previously known results.
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stability analysis of impulsive cohen grossberg neural networks with distributed delays and reaction diffusion terms
Applied Mathematical Modelling, 2009Co-Authors: Zuoan Li, Kelin LiAbstract:Abstract In this paper, we investigate a class of impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms. By establishing an integro-Differential Inequality with impulsive initial conditions and applying M -matrix theory, we find some sufficient conditions ensuring the existence, uniqueness, global exponential stability and global robust exponential stability of equilibrium point for impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms. An example is given to illustrate the results obtained here.
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exponential stability of impulsive cohen grossberg neural networks with time varying delays and reaction diffusion terms
Neurocomputing, 2008Co-Authors: Kelin Li, Qiankun SongAbstract:In this paper, we investigate a class of impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. By establishing a delay Differential Inequality with impulsive initial conditions and employing M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. In particular, the estimate of the exponential convergence rate is also provided, which depends on the system parameters and delays. Two examples are given to illustrate the results obtained here.
Tingwen Huang - One of the best experts on this subject based on the ideXlab platform.
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impulsive delayed integro Differential Inequality and its application on imnns with discrete and distributed delays
Neurocomputing, 2019Co-Authors: Huamin Wang, Tingwen Huang, Jie Tan, Shukai DuanAbstract:Abstract Impulsive Differential Inequality is crucial for nonlinear impulsive Differential system. In this paper, an impulsive delayed integro-Differential Inequality is firstly presented and studied. New impulsive Inequality is given by M-matrix, proof by contradiction and recursive Inequality. Then, the discrete-distributed delayed memristive neural network model is constructed and its exponential stability criteria are obtained by the novel impulsive delayed Inequality. Finally, two numerical examples are given to illustrate the effectiveness of exponential stability criteria.
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novel existence and stability criteria of periodic solutions for impulsive delayed neural networks via coefficient integral averages
Neurocomputing, 2016Co-Authors: Huamin Wang, Tingwen Huang, Shukai Duan, Lidan WangAbstract:Abstract In this paper, an impulsive neural network with periodic coefficients and delayed time is firstly given, then the existence, uniqueness and exponential stability problems of periodic solutions for this system are further investigated. By means of the characteristic equation of delayed Differential equations and impulsive Differential equations, an impulsive delayed Differential Inequality with novel conditions are constructed. It can be used as an impulsive comparison system to deal with the existence and exponential stability problems. By utilizing the impulsive comparison system, Lyapunov functions and the fixed point theorem, we obtain the periodic solution's existence and exponential stability criteria. Finally, one numerical example and its simulations are given to illustrate the effectiveness of the theoretical results.
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exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control
Neural Networks, 2016Co-Authors: Shiju Yang, Chuandong Li, Tingwen HuangAbstract:The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and Differential Inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results.
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robust stability of delayed fuzzy cohen grossberg neural networks
Computers & Mathematics With Applications, 2011Co-Authors: Tingwen HuangAbstract:In this paper, using a linear matrix Inequality and a Differential Inequality, we obtain an exponentially robust stability criterion for interval fuzzy Cohen-Grossberg type neural networks with time-varying delays. The criterion can be easily applied to the design of fuzzy general neural networks.
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synchronization of delayed chaotic systems with parameter mismatches by using intermittent linear state feedback
Nonlinearity, 2009Co-Authors: Tingwen Huang, Guanrong ChenAbstract:This paper investigates the synchronization of coupled chaotic systems with time delay in the presence of parameter mismatches by using intermittent linear state feedback control. Quasi-synchronization criteria are obtained by means of a Lyapunov function and the Differential Inequality method. Numerical simulations on the chaotic systems are presented to demonstrate the effectiveness of the theoretical results.
Jinde Cao - One of the best experts on this subject based on the ideXlab platform.
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stability analysis of nicholson s blowflies equation with two different delays
Mathematics and Computers in Simulation, 2020Co-Authors: Chuangxia Huang, Xiaoguang Yang, Jinde CaoAbstract:Abstract This paper investigates an autonomous Nicholson’s blowflies equation incorporating two different delays. By using Differential Inequality techniques and dynamical system approaches, we establish two novel criteria to check the global exponential stability and asymptotical stability on the zero equilibrium point of the addressed equation, respectively. Our research partially answers an open question raised by Berezansky and Braverman (2017). A numerical example with simulations shows that the main theoretical results are correct.
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p th moment exponential input to state stability of delayed recurrent neural networks with markovian switching via vector lyapunov function
IEEE Transactions on Neural Networks, 2018Co-Authors: Lei Liu, Jinde Cao, Cheng QianAbstract:In this paper, the $p$ th moment input-to-state exponential stability for delayed recurrent neural networks (DRNNs) with Markovian switching is studied. By using stochastic analysis techniques and classical Razumikhin techniques, a generalized vector $ {\mathcal {L}}$ -operator Differential Inequality including cross item is obtained. Without additional restrictive conditions on the time-varying delay, the sufficient criteria on the $p$ th moment input-to-state exponential stability for DRNNs with Markovian switching are derived by means of the vector $ {\mathcal {L}}$ -operator Differential Inequality. When the input is zero, an improved criterion on exponential stability is obtained. Two numerical examples are provided to examine the correctness of the derived results.
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existence and global exponential stability of pseudo almost periodic solution for neutral delay bam neural networks with time varying delay in leakage terms
Chaos Solitons & Fractals, 2018Co-Authors: Chaouki Aouiti, Jinde Cao, Imen Ben Gharbia, Mohammed Salah Mhamdi, Ahmed AlsaediAbstract:Abstract In this paper, bidirectional associative memory (BAM) neural networks with time-varying delays in leakage terms are investigated. A set of sufficient conditions are obtained for the existence and the exponential stability of pseudo almost periodic solutions for this class of neural networks by applying Banach’s fixed point theorems, and Differential Inequality techniques. Finally, we present a numerical example and simulation to illustrate the effectiveness of the theoretical results. We extend and improve previously know results.
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Piecewise Pseudo Almost Periodic Solution for Impulsive Generalised High-Order Hopfield Neural Networks with Leakage Delays
Neural Processing Letters, 2016Co-Authors: Chaouki Aouiti, Mohammed Salah M’hamdi, Jinde Cao, Ahmed AlsaediAbstract:Existence of piecewise differentiable pseudo almost-periodic solutions for a class of impulsive high-order Hopfield neural networks with leakage delays are established by employing the fixed point theorem, Differential Inequality and Lyapunov functionals. The results of this paper are new and they supplement previously known works. Numerical example with graphical illustration is given to illuminate our main results.
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p th moment exponential stochastic synchronization of coupled memristor based neural networks with mixed delays via delayed impulsive control
Neural Networks, 2015Co-Authors: Xinsong Yang, Jinde Cao, Jianlong QiuAbstract:This paper concerns the p th moment synchronization in an array of generally coupled memristor-based neural networks with time-varying discrete delays, unbounded distributed delays, as well as stochastic perturbations. Hybrid controllers are designed to cope with the uncertainties caused by the state-dependent parameters: (a) state feedback controllers combined with delayed impulsive controller; (b) adaptive controller combined with delayed impulsive controller. Based on an impulsive Differential Inequality, the properties of random variables, the framework of Filippov solution, and Lyapunov functional method, sufficient conditions are derived to guarantee that the considered coupled memristor-based neural networks can be p th moment globally exponentially synchronized onto an isolated node under both of the two classes of hybrid impulsive controllers. Finally, numerical simulations are given to show the effectiveness of the theoretical results.
Qiankun Song - One of the best experts on this subject based on the ideXlab platform.
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exponential stability of impulsive cohen grossberg neural networks with time varying delays and reaction diffusion terms
Neurocomputing, 2008Co-Authors: Kelin Li, Qiankun SongAbstract:In this paper, we investigate a class of impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. By establishing a delay Differential Inequality with impulsive initial conditions and employing M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. In particular, the estimate of the exponential convergence rate is also provided, which depends on the system parameters and delays. Two examples are given to illustrate the results obtained here.
Xinsong Yang - One of the best experts on this subject based on the ideXlab platform.
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p th moment exponential stochastic synchronization of coupled memristor based neural networks with mixed delays via delayed impulsive control
Neural Networks, 2015Co-Authors: Xinsong Yang, Jinde Cao, Jianlong QiuAbstract:This paper concerns the p th moment synchronization in an array of generally coupled memristor-based neural networks with time-varying discrete delays, unbounded distributed delays, as well as stochastic perturbations. Hybrid controllers are designed to cope with the uncertainties caused by the state-dependent parameters: (a) state feedback controllers combined with delayed impulsive controller; (b) adaptive controller combined with delayed impulsive controller. Based on an impulsive Differential Inequality, the properties of random variables, the framework of Filippov solution, and Lyapunov functional method, sufficient conditions are derived to guarantee that the considered coupled memristor-based neural networks can be p th moment globally exponentially synchronized onto an isolated node under both of the two classes of hybrid impulsive controllers. Finally, numerical simulations are given to show the effectiveness of the theoretical results.
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existence and global exponential stability of periodic solution for cohen grossberg shunting inhibitory cellular neural networks with delays and impulses
Neurocomputing, 2009Co-Authors: Xinsong YangAbstract:By using Schaeffer's theorem, Differential Inequality techniques, and constructing suitable Lyapunov functional, several sufficient conditions are obtained for the existence and global exponential stability of periodic solution for impulsive Cohen-Grossberg shunting inhibitory cellular neural networks with delays. The results of this paper are completely new and complement and improve some of the previously known results. An example is employed to illustrate our feasible results.