The Experts below are selected from a list of 118872 Experts worldwide ranked by ideXlab platform
Qiankun Song - One of the best experts on this subject based on the ideXlab platform.
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boundedness and global robust Stability analysis of delayed complex valued neural networks with interval parameter uncertainties
Neural Networks, 2018Co-Authors: Qiankun Song, Zhenjiang Zhao, Yurong Liu, Fuad E AlsaadiAbstract:Abstract In this paper, the boundedness and robust Stability for a class of delayed complex-valued neural networks with interval parameter uncertainties are investigated. By using Homomorphic mapping theorem, Lyapunov method and inequality techniques, sufficient condition to guarantee the boundedness of networks and the existence, uniqueness and global robust Stability of Equilibrium point is derived for the considered uncertain neural networks. The obtained robust Stability criterion is expressed in complex-valued LMI, which can be calculated numerically using YALMIP with solver of SDPT3 in MATLAB. An example with simulations is supplied to show the applicability and advantages of the acquired result.
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global exponential Stability of impulsive complex valued neural networks with both asynchronous time varying and continuously distributed delays
Neural Networks, 2016Co-Authors: Qiankun Song, Zhenjiang Zhao, Yurong Liu, Huan YanAbstract:This paper investigates the Stability problem for a class of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays. By employing the idea of vector Lyapunov function, M-matrix theory and inequality technique, several sufficient conditions are obtained to ensure the global exponential Stability of Equilibrium point. When the impulsive effects are not considered, several sufficient conditions are also given to guarantee the existence, uniqueness and global exponential Stability of Equilibrium point. Two examples are given to illustrate the effectiveness and lower level of conservatism of the proposed criteria in comparison with some existing results.
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global exponential Stability of complex valued neural networks with both time varying delays and impulsive effects
Neural Networks, 2016Co-Authors: Qiankun Song, Zhenjiang ZhaoAbstract:In this paper, the global exponential Stability of complex-valued neural networks with both time-varying delays and impulsive effects is discussed. By employing Lyapunov functional method and using matrix inequality technique, several sufficient conditions in complex-valued linear matrix inequality form are obtained to ensure the existence, uniqueness and global exponential Stability of Equilibrium point for the considered neural networks. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The proposed Stability results are less conservative than some recently known ones in the literatures, which is demonstrated via two examples with simulations.
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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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exponential Stability of recurrent neural networks with both time varying delays and general activation functions via lmi approach
Neurocomputing, 2008Co-Authors: Qiankun SongAbstract:In this paper, the problem on exponential Stability analysis of recurrent neural networks with both time-varying delays and general activation functions is considered. Neither the boundedness and the monotony on these activation functions nor the differentiability on the time-varying delays are assumed. By employing Lyapunov functional and the free-weighting matrix method, several sufficient conditions in linear matrix inequality form are obtained to ensure the existence, uniqueness and global exponential Stability of Equilibrium point for the neural networks. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The proposed Stability results are less conservative than some recently known ones in the literature, which is demonstrated via an example with simulation.
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.
Zhengqiu Zhang - One of the best experts on this subject based on the ideXlab platform.
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lmi based global exponential Stability of Equilibrium point for neutral delayed bam neural networks with delays in leakage terms via new inequality technique
Neurocomputing, 2016Co-Authors: Wenli Peng, Zhengqiu ZhangAbstract:The paper is concerned with a class of neutral BAM neural networks with delays in leakage terms. The existence of Equilibrium point dependent on time delays of system (1.1) is obtained by establishing and applying two new inequalities. Sufficient condition is established to ensure the global exponential Stability of the above neutral networks by establishing and employing above two new inequalities and new LMI methods. The results of this paper are new and complementary to the previously known results.
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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 exponential Stability via inequality technique for inertial bam neural networks with time delays
Neurocomputing, 2015Co-Authors: Zhengqiu Zhang, Zhiyong QuanAbstract:Abstract In this paper, the existence and global exponential Stability of Equilibrium point for inertial BAM neural networks with time delays are discussed. Firstly, by using homeomorphism theory and inequality technique, the LMI-based sufficient condition on the existence and uniqueness of Equilibrium point for above inertial BAM neural networks is obtained. Secondly, a LMI-based condition which can ensure the global exponential Stability of Equilibrium point for the system is obtained by using LMI method and inequality technique. In our results, the boundedness assumption on the activation functions in Ke and Miao (2013) [19] , [20] is removed. Hence, our result on global exponential Stability of Equilibrium point for above system is less conservative.
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novel lmi based condition on global asymptotic Stability for bam neural networks with reaction diffusion terms and distributed delays
Neurocomputing, 2014Co-Authors: Zhiyong Quan, Lihong Huang, Zhengqiu ZhangAbstract:Abstract In this paper, under the assumption that the activation functions only satisfy global Lipschitz conditions, a novel LMI-based sufficient condition for global asymptotic Stability of Equilibrium point of a class of BAM neural networks with reaction–diffusion terms and distributed delays is obtained by using degree theory, LMI method, inequalities technique and constructing Lyapunov functionals. In our results, the assumptions for boundedness and monotonicity in existing papers on the activation functions are removed.
Yurong Liu - One of the best experts on this subject based on the ideXlab platform.
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boundedness and global robust Stability analysis of delayed complex valued neural networks with interval parameter uncertainties
Neural Networks, 2018Co-Authors: Qiankun Song, Zhenjiang Zhao, Yurong Liu, Fuad E AlsaadiAbstract:Abstract In this paper, the boundedness and robust Stability for a class of delayed complex-valued neural networks with interval parameter uncertainties are investigated. By using Homomorphic mapping theorem, Lyapunov method and inequality techniques, sufficient condition to guarantee the boundedness of networks and the existence, uniqueness and global robust Stability of Equilibrium point is derived for the considered uncertain neural networks. The obtained robust Stability criterion is expressed in complex-valued LMI, which can be calculated numerically using YALMIP with solver of SDPT3 in MATLAB. An example with simulations is supplied to show the applicability and advantages of the acquired result.
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global exponential Stability of impulsive complex valued neural networks with both asynchronous time varying and continuously distributed delays
Neural Networks, 2016Co-Authors: Qiankun Song, Zhenjiang Zhao, Yurong Liu, Huan YanAbstract:This paper investigates the Stability problem for a class of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays. By employing the idea of vector Lyapunov function, M-matrix theory and inequality technique, several sufficient conditions are obtained to ensure the global exponential Stability of Equilibrium point. When the impulsive effects are not considered, several sufficient conditions are also given to guarantee the existence, uniqueness and global exponential Stability of Equilibrium point. Two examples are given to illustrate the effectiveness and lower level of conservatism of the proposed criteria in comparison with some existing results.
Zhenjiang Zhao - One of the best experts on this subject based on the ideXlab platform.
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boundedness and global robust Stability analysis of delayed complex valued neural networks with interval parameter uncertainties
Neural Networks, 2018Co-Authors: Qiankun Song, Zhenjiang Zhao, Yurong Liu, Fuad E AlsaadiAbstract:Abstract In this paper, the boundedness and robust Stability for a class of delayed complex-valued neural networks with interval parameter uncertainties are investigated. By using Homomorphic mapping theorem, Lyapunov method and inequality techniques, sufficient condition to guarantee the boundedness of networks and the existence, uniqueness and global robust Stability of Equilibrium point is derived for the considered uncertain neural networks. The obtained robust Stability criterion is expressed in complex-valued LMI, which can be calculated numerically using YALMIP with solver of SDPT3 in MATLAB. An example with simulations is supplied to show the applicability and advantages of the acquired result.
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global exponential Stability of impulsive complex valued neural networks with both asynchronous time varying and continuously distributed delays
Neural Networks, 2016Co-Authors: Qiankun Song, Zhenjiang Zhao, Yurong Liu, Huan YanAbstract:This paper investigates the Stability problem for a class of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays. By employing the idea of vector Lyapunov function, M-matrix theory and inequality technique, several sufficient conditions are obtained to ensure the global exponential Stability of Equilibrium point. When the impulsive effects are not considered, several sufficient conditions are also given to guarantee the existence, uniqueness and global exponential Stability of Equilibrium point. Two examples are given to illustrate the effectiveness and lower level of conservatism of the proposed criteria in comparison with some existing results.
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global exponential Stability of complex valued neural networks with both time varying delays and impulsive effects
Neural Networks, 2016Co-Authors: Qiankun Song, Zhenjiang ZhaoAbstract:In this paper, the global exponential Stability of complex-valued neural networks with both time-varying delays and impulsive effects is discussed. By employing Lyapunov functional method and using matrix inequality technique, several sufficient conditions in complex-valued linear matrix inequality form are obtained to ensure the existence, uniqueness and global exponential Stability of Equilibrium point for the considered neural networks. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The proposed Stability results are less conservative than some recently known ones in the literatures, which is demonstrated via two examples with simulations.
