共查询到20条相似文献,搜索用时 15 毫秒
1.
Quanjun WuAuthor Vitae Jin ZhouAuthor Vitae Lan XiangAuthor Vitae 《Neurocomputing》2011,74(17):3204-3211
The present paper formulates and studies a model of recurrent neural networks with time-varying delays in the presence of impulsive connectivity among the neurons. This model can well describe practical architectures of more realistic neural networks. Some novel yet generic criteria for global exponential stability of such neural networks are derived by establishing an extended Halanay differential inequality on impulsive delayed dynamical systems. The distinctive feature of this work is to address exponential stability issues without a priori stability assumption for the corresponding delayed neural networks without impulses. It is shown that the impulses in neuronal connectivity play an important role in inducing global exponential stability of recurrent delayed neural networks even if it may be unstable or chaotic itself. Furthermore, example and simulation are given to illustrate the practical nature of the novel results. 相似文献
2.
This paper focuses on the problem of exponential stability in the sense of Lagrange for impulses in discrete-time delayed recurrent neural networks. By establishing a delayed impulsive discrete inequality and a novel difference inequality, combining with inequality techniques, some novel sufficient conditions are obtained to ensure exponential Lagrange stability for impulses in discrete-time delayed recurrent neural networks. Meanwhile, exponentially convergent scope of neural network is given. Finally, several numerical simulations are given to demonstrate the effectiveness of our results. 相似文献
3.
In this paper, the global exponential stability is investigated for the bi-directional associative memory networks with time delays. Several new sufficient conditions are presented to ensure global exponential stability of delayed bi-directional associative memory neural networks based on the Lyapunov functional method as well as linear matrix inequality technique. To the best of our knowledge, few reports about such “linearization” approach to exponential stability analysis for delayed neural network models have been presented in literature. The method, called parameterized first-order model transformation, is used to transform neural networks. The obtained conditions show to be less conservative and restrictive than that reported in the literature. Two numerical simulations are also given to illustrate the efficiency of our result. 相似文献
4.
Robust global exponential stability of Cohen-Grossberg neural networks with time delays 总被引:11,自引:0,他引:11
Tianping Chen Libin Rong 《Neural Networks, IEEE Transactions on》2004,15(1):203-206
The authors discuss delayed Cohen-Grossberg neural network models and investigate their global exponential stability of the equilibrium point for the systems. A set of sufficient conditions ensuring robust global exponential convergence of the Cohen-Grossberg neural networks with time delays are given. 相似文献
5.
Yu ZhangAuthor Vitae 《Neurocomputing》2011,74(17):3268-3276
In this paper, the robust exponential stability of uncertain impulsive neural networks with time-varying delays and delayed impulses is considered. It is assumed that the considered impulsive neural networks have norm-bounded parametric uncertainties and time-varying delays and the state variables on the impulses may relate to the time-varying delays. By using Lyapunov functions together with Razumikhin technique or with differential inequalities, some new robust exponential stability criteria are provided. Some examples and their simulations, including examples that the stability of which can not be tackled by the existing results, are also presented to illustrate the effectiveness and the advantage of the obtained results. 相似文献
6.
In this paper, the global robust exponential stability of equilibrium solution to delayed reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales is studied. Using topological degree theory, M-matrix method, Lyapunov functional and inequality skills, we establish some sufficient conditions for the existence, uniqueness and global robust exponential stability of equilibrium solution to delayed reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales. One example is given to illustrate the effectiveness of our results. 相似文献
7.
This paper investigates the problem of the existence and global exponential stability of the periodic solution of memristor-based delayed network. Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based recurrent networks is established. Several sufficient conditions are obtained, which ensure the existence of periodic solutions and global exponential stability of the memristor-based delayed recurrent networks. These results ensure global exponential stability of memristor-based network in the sense of Filippov solutions. And, it is convenient to estimate the exponential convergence rates of this network by the results. An illustrative example is given to show the effectiveness of the theoretical results. 相似文献
8.
Neural Processing Letters - A class of global exponential synchronization problem for delayed quaternion-valued neural networks with stochastic impulses has been investigated in this paper, where... 相似文献
9.
Chao LiuAuthor VitaeChuandong LiAuthor Vitae Xiaofeng LiaoAuthor Vitae 《Neurocomputing》2011,74(17):3286-3295
In this paper, the globally exponential stability of BAM neural networks with time delays and impulses has been studied. Different from most existing publications, the case of variable time impulses is dealt with in the present paper, i.e., impulse occurring is not at fixed instants but depends on the states of systems. By using Lyapunov function and inequality technique, some globally exponential stability criteria of BAM neural networks with time delays and variable-time impulses have been established. When the proposed results can also be applied to the case of fixed-time impulses, it provides new stability conditions for the case of fixed-time impulses. Numerical examples are also given to illustrate the effectiveness of our theoretical results. 相似文献
10.
具时滞脉冲细胞神经网络的全局指数稳定性 总被引:2,自引:0,他引:2
研究了一类新的具有脉冲的时滞细胞神经网络系统模型,引入了一类新的脉冲条件,在不假设激励函数的有界性、单调性和光滑性的条件下,得到了系统平衡点的存在性、唯一性及全局指数稳定性的一些新的充分条件,并得到了指数收敛速率. 相似文献
11.
《Neural Networks, IEEE Transactions on》2010,21(1):67-81
12.
This paper studies stationary oscillation for a time-varying recurrent cellular neural network with time delays and impulses. In a recent paper, the authors claim that they obtain a criterion of existence, uniqueness, and global exponential stability of periodic solution (i.e. stationary oscillation) for a recurrent cellular neural network with time delays and impulses. We point out that the main result of their paper is incorrect, and present a sufficient condition of stationary oscillation for a time-varying recurrent cellular neural networks with time delays and impulses. An numerical example is given to illustrate the effectiveness of the obtained result. 相似文献
13.
14.
In this paper, a class of non-autonomous reaction-diffusion neural networks with time-varying delays is considered. Novel methods to study the global dynamical behavior of these systems are proposed. Employing the properties of diffusion operator and the method of delayed inequalities analysis, we investigate global exponential stability, positive invariant sets and global attracting sets of the neural networks under consideration. Furthermore, conditions sufficient for the existence and uniqueness of periodic attractors for periodic neural networks are derived and the existence range of the attractors is estimated. Finally two examples are given to demonstrate the effectiveness of these results. 相似文献
15.
Estimate of exponential convergence rate and exponential stabilityfor neural networks 总被引:5,自引:0,他引:5
Estimates of exponential convergence rate and exponential stability are studied for a class of neural networks which includes Hopfield neural networks and cellular neural networks. Both local and global exponential convergence are discussed. Theorems for estimation of the exponential convergence rate are established and the bounds on the rate of convergence are given. The domains of attraction in the case of local exponential convergence are obtained. Simple conditions are presented for checking exponential stability of the neural networks. 相似文献
16.
《国际计算机数学杂志》2012,89(10):2188-2201
The article addresses the problem of global robust exponential stability of interval neural networks with time-varying delays. On the basis of linear matrix inequality technique and M-matrix theory, some novel sufficient conditions for the existence, uniqueness, and global robust exponential stability of the equilibrium point for delayed interval neural networks are presented. It is shown that our results improve and generalize some previously published ones. Some numerical examples and simulations are given to show the effectiveness of the obtained results. 相似文献
17.
Stability analysis of dynamical neural networks 总被引:9,自引:0,他引:9
In this paper, we use the matrix measure technique to study the stability of dynamical neural networks. Testable conditions for global exponential stability of nonlinear dynamical systems and dynamical neural networks are given. It shows how a few well-known results can be unified and generalized in a straightforward way. Local exponential stability of a class of dynamical neural networks is also studied; we point out that the local exponential stability of any equilibrium point of dynamical neural networks is equivalent to the stability of the linearized system around that equilibrium point. From this, some well-known and new sufficient conditions for local exponential stability of neural networks are obtained 相似文献
18.
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. 相似文献
19.
This paper investigates global exponential stability of a class of Hopfield neural networks with delays based on contraction
mapping principle, Lyapunov function and inequality technique. Some sufficient conditions are derived that ensure the existence,
uniqueness, global exponential stability of equilibrium point of the neural networks. Finally, an illustrative numerical example
is given to demonstrate the effectiveness of our results. 相似文献