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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.
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.
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global exponential stability of impulsive cohen Grossberg neural network with time varying delays
Nonlinear Analysis-real World Applications, 2008Co-Authors: Qiankun Song, Jiye ZhangAbstract:In this paper, the impulsive Cohen–Grossberg neural network model with time-varying delays is considered. Applying the idea of vector Lyapunov function, M-matrix theory and inequality technique, several new sufficient conditions are obtained to ensure global exponential stability of equilibrium point for impulsive Cohen–Grossberg neural network with time-varying delays. These results generalize a few previous known results and remove some restrictions on the neural network. An example is given to show the effectiveness of the obtained results. It is believed that these results are significant and useful for the design and applications of the Cohen–Grossberg neural network.
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Global exponential stability of impulsive Cohen–Grossberg neural network with time-varying delays
Nonlinear Analysis-real World Applications, 2008Co-Authors: Qiankun Song, Jiye ZhangAbstract:In this paper, the impulsive Cohen–Grossberg neural network model with time-varying delays is considered. Applying the idea of vector Lyapunov function, M-matrix theory and inequality technique, several new sufficient conditions are obtained to ensure global exponential stability of equilibrium point for impulsive Cohen–Grossberg neural network with time-varying delays. These results generalize a few previous known results and remove some restrictions on the neural network. An example is given to show the effectiveness of the obtained results. It is believed that these results are significant and useful for the design and applications of the Cohen–Grossberg neural network.
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stability analysis of cohen Grossberg neural network with both time varying and continuously distributed delays
Journal of Computational and Applied Mathematics, 2006Co-Authors: Qiankun SongAbstract:In this paper, the Cohen-Grossberg neural network model with both time-varying and continuously distributed delays is considered. Without assuming both global Lipschitz conditions on these activation functions and the differentiability on these time-varying delays, applying the idea of vector Lyapunov function, M-matrix theory and inequality technique, several new sufficient conditions are obtained to ensure the existence, uniqueness, and global exponential stability of equilibrium point for Cohen-Grossberg neural network with both time-varying and continuously distributed delays. These results generalize and improve the earlier publications. Two numerical examples are given to show the effectiveness of the obtained results. It is believed that these results are significant and useful for the design and applications of the Cohen-Grossberg neural networks.
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global exponential robust stability of cohen Grossberg neural network with time varying delays and reaction diffusion terms
Journal of The Franklin Institute-engineering and Applied Mathematics, 2006Co-Authors: Qiankun SongAbstract:Abstract In this paper, the global exponential robust stability is investigated for Cohen–Grossberg neural network with time-varying delays and reaction–diffusion terms, this neural network contains time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. Neither the boundedness and differentiability on the activation functions nor the differentiability on the time-varying delays are assumed. By using general Halanay inequality and M-matrix theory, several new sufficient conditions are obtained to ensure the existence, uniqueness, and global exponential robust stability of equilibrium point for Cohen–Grossberg neural network with time-varying delays and reaction–diffusion terms. Several previous results are improved and generalized, and three examples are given to show the effectiveness of the obtained results.
Cheng Hu - One of the best experts on this subject based on the ideXlab platform.
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finite time synchronization of memristor based cohen Grossberg neural networks with time varying delays
Neurocomputing, 2016Co-Authors: Haijun Jiang, Cheng HuAbstract:This paper concerns the problem of global and local finite-time synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying delays by designing an appropriate feedback controller. Through a nonlinear transformation, we derive an alternative system from the considered memristor-based Cohen-Grossberg neural networks. Then, by considering the finite-time synchronization of the alternative system, we obtain some novel and effective finite-time synchronization criteria for the considered memristor-based Cohen-Grossberg neural networks. These results generalize and extend some previous known works on conventional Cohen-Grossberg neural networks. Finally, numerical simulations are given to present the effectiveness of the theoretical results.
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finite time synchronization of delayed neural networks with cohen Grossberg type based on delayed feedback control
Neurocomputing, 2014Co-Authors: Cheng Hu, Juan Yu, Haijun JiangAbstract:This paper is concerned with finite-time synchronization for a class of delayed neural networks with Cohen-Grossberg type. Different from the existing related results, the time-delayed feedback strategy is utilized to investigate finite-time synchronization of delayed Cohen-Grossberg neural networks. By constructing Lyapunov functions and using differential inequalities, several new and effective criteria are derived to realize local and global synchronization in finite time of the addressed neural networks based on two different time-delayed feedback controllers. Besides, the upper bounds of the settling time of synchronization are estimated. Furthermore, as corollaries, some sufficient conditions are given to achieve finite-time synchronization of delayed cellular neural networks. Finally, some numerical examples are provided to verify the theoretical results established in this paper.
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exponential synchronization of cohen Grossberg neural networks via periodically intermittent control
Neurocomputing, 2011Co-Authors: Juan Yu, Haijun Jiang, Cheng Hu, Zhidong TengAbstract:In this paper, a class of Cohen-Grossberg neural networks with time-varying delays are studied by designing a periodically intermittent controller. Some novel and effective exponential synchronization criteria are derived by applying some analysis techniques. These results generalize a few previous known results and remove some restrictions on control width and time-delays. Finally, a chaotic Cohen-Grossberg neural network is represented to show the effectiveness and feasibility of our results.
Haijun Jiang - One of the best experts on this subject based on the ideXlab platform.
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finite time synchronization of memristor based cohen Grossberg neural networks with time varying delays
Neurocomputing, 2016Co-Authors: Haijun Jiang, Cheng HuAbstract:This paper concerns the problem of global and local finite-time synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying delays by designing an appropriate feedback controller. Through a nonlinear transformation, we derive an alternative system from the considered memristor-based Cohen-Grossberg neural networks. Then, by considering the finite-time synchronization of the alternative system, we obtain some novel and effective finite-time synchronization criteria for the considered memristor-based Cohen-Grossberg neural networks. These results generalize and extend some previous known works on conventional Cohen-Grossberg neural networks. Finally, numerical simulations are given to present the effectiveness of the theoretical results.
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function projective synchronization of memristor based cohen Grossberg neural networks with time varying delays
Cognitive Neurodynamics, 2015Co-Authors: Abdujelil Abdurahman, Haijun Jiang, Kaysar RahmanAbstract:This paper deals with the problem of function projective synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying delays. Based on the theory of differential equations with discontinuous right-hand side, some novel criteria are obtained to realize the function projective synchronization of addressed networks by combining open loop control and linear feedback control. As some special cases, several control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization of the considered memristor-based Cohen–Grossberg neural network. Finally, a numerical example and its simulations are provided to demonstrate the effectiveness of the obtained results.
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finite time synchronization of delayed neural networks with cohen Grossberg type based on delayed feedback control
Neurocomputing, 2014Co-Authors: Cheng Hu, Juan Yu, Haijun JiangAbstract:This paper is concerned with finite-time synchronization for a class of delayed neural networks with Cohen-Grossberg type. Different from the existing related results, the time-delayed feedback strategy is utilized to investigate finite-time synchronization of delayed Cohen-Grossberg neural networks. By constructing Lyapunov functions and using differential inequalities, several new and effective criteria are derived to realize local and global synchronization in finite time of the addressed neural networks based on two different time-delayed feedback controllers. Besides, the upper bounds of the settling time of synchronization are estimated. Furthermore, as corollaries, some sufficient conditions are given to achieve finite-time synchronization of delayed cellular neural networks. Finally, some numerical examples are provided to verify the theoretical results established in this paper.
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exponential synchronization of cohen Grossberg neural networks via periodically intermittent control
Neurocomputing, 2011Co-Authors: Juan Yu, Haijun Jiang, Cheng Hu, Zhidong TengAbstract:In this paper, a class of Cohen-Grossberg neural networks with time-varying delays are studied by designing a periodically intermittent controller. Some novel and effective exponential synchronization criteria are derived by applying some analysis techniques. These results generalize a few previous known results and remove some restrictions on control width and time-delays. Finally, a chaotic Cohen-Grossberg neural network is represented to show the effectiveness and feasibility of our results.
Zhengqiu Zhang - One of the best experts on this subject based on the ideXlab platform.
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global asymptotic stability for a class of complex valued cohen Grossberg neural networks with time delays
Neurocomputing, 2016Co-Authors: Zhengqiu Zhang, Shenghua YuAbstract:In this paper, we are concerned with the existence, uniqueness and global asymptotic stability of equilibrium point for a class of complex-valued Cohen-Grossberg neural networks with time delays. By using homeomorphism theory, new matrix inequality techniques and inequality techniques, several LMI-based sufficient conditions on the existence, uniqueness and global asymptotic stability of equilibrium point for above complex-valued Cohen-Grossberg neural networks with two classes of complex-valued activation functions, behaved functions and amplification functions are obtained. So far, only the stabilities of complex-valued recurrent neural networks, Hopfield neural networks and Cellular neural networks have been studied. Hence, it is for the first place that the stability of complex-valued Cohen-Grossberg neural networks is discussed in this paper.
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global asymptotic stability to a generalized cohen Grossberg bam neural networks of neutral type delays
Neural Networks, 2012Co-Authors: Zhengqiu Zhang, Dongming ZhouAbstract:In this paper, we first discuss the existence of a unique equilibrium point of a generalized Cohen-Grossberg BAM neural networks of neutral type delays by means of the Homeomorphism theory and inequality technique. Then, by applying the existence result of an equilibrium point and constructing a Lyapunov functional, we study the global asymptotic stability of the equilibrium solution to the above Cohen-Grossberg BAM neural networks of neutral type. In our results, the hypothesis for boundedness in the existing paper, which discussed Cohen-Grossberg neural networks of neutral type on the activation functions, are removed. Finally, we give an example to demonstrate the validity of our global asymptotic stability result for the above neural networks.
