随机扰动神经网络的脉冲控制

脉冲控制具有响应速度快,鲁棒性和抗干扰能力好的特点,被广泛应用于参数随机扰动的动力学系统的控制.本文研究一类参数随机扰动的变时滞细胞神经网络在脉冲控制下的全局指数稳定性问题.利用Ly印unov稳定性理论和离散Halanay不等式技术手段,分别给出在脉冲控制下,参数随机扰动和无参数扰动的变时滞细胞神经网络全局指数稳定的充分条件.最后,通过数值算例说明所得结果.     

具S-型分布时滞的模糊细胞神经网络的周期解及指数稳定性

研究了一类具S-型分布时滞的模糊细胞神经网络(FCNN)的周期解及全局指数稳定性问题.在不要求激励函数全局L ipsch itz条件下,通过使用指数型二分性和Schauder不动点定理以及构造Lyapunov函数,得到了模糊细胞神经网络模型周期解和指数稳定性的一些充分条件.此外,给出一个实例说明结果是可行的.     

带有时变时滞的中立型神经网络的鲁棒指数稳定性

研究一类带有时变时滞的中立型神经网络的全局指数稳定性问题.通过构造LyapunovKrasovskii泛函并使用线性矩阵不等式方法,建立了保障时滞神经网络全局指数稳定的新的时滞相关充分条件.这些条件用线性矩阵不等式表达.进一步,文章对一类不确定时滞中立型神经网络给出了鲁棒全局指数稳定的新判据.     

具分布时滞模糊BAM神经网络的指数稳定性

介绍了一类具分布时滞的模糊BAM(bi-directional associative memory)神经网络.通过构造Lyapunov-Krasovskii泛函及利用线性矩阵不等式方法,得到了此类系统平衡点的指数稳定性的一个充分条件.在设计具时滞的人工BAM神经网络时,全局指数稳定的结果有很重要的意义.此外,给出一个实例说明我们的结果是可行的.     

含混合时滞和脉冲的Cohen-Grossberg神经网络的全局指数稳定性

本文讨论了含混合时滞和脉冲的Cohen-Grossberg神经网络的稳定性.通过应用M矩阵理论和不等式技巧,得到了含混合时滞的Cohen-Grossberg神经网络平衡态的全局指数稳定性的充分条件.相比以前同类文献,本文减弱了部分条件,推广了部分结论,并在文末给出了两个示例.本文结论对于设计和应用神经网络有一定实用价值.     

具有界时滞和分布时滞的脉冲细胞神经网络动力系统的全局指数稳定性

基于现有文献大多研究线性脉冲动力系统,对具非线性脉冲影响的研究较少的情况,主要利用拓扑度理论,M-矩阵理论,Liapunov泛函方法,研究了具有界时滞和分布时滞的一类细胞神经网络动力系统的非线性脉冲影响,获得了其平衡点全局指数稳定性的充分条件.     

变时滞Hopfield神经网络的全局指数稳定性

本文研究了一类具有变时滞Hopfield神经网络的稳定性问题.利用时滞微分不等式方法,获得了几个关于该网络的全局指数稳定性与时滞无关的充分条件,并且给出了此类网络的收敛指数的估计,推广了已知文献的结果.最后给出数值例子证明结论的有效性.     

高阶变时滞广义细胞神经网络的全局指数周期性

研究了高阶变时滞广义细胞神经网络的全局指数周期性.引入可调参数和通过构造合适的Lyapunov泛函并利用Brouwer压缩映象原理,得到了神经网络周期解存在唯一且全局指数周期与全局指数稳定的充分条件,并给出一个例子说明结论的有效性.     

在时间尺度上研究带有脉冲的泄漏时滞细胞神经网络反周期解的动态特征

在时间尺度上,通过使用叠合度理论中的连续定理和微分不等式技巧,研究带有脉冲的时变泄漏时滞细胞神经网络模型的反周期解,获得了一些使带有脉冲的时变泄漏时滞细胞神经网络模型的反周期解存在和全局指数稳定的充分条件,并将以前的结论在时间尺度上做了扩展.     

具有变时滞脉冲效应的Hopfield神经网络的全局指数稳定性

研究了一类新的具有脉冲的时滞Hopfield神经网络系统模型,引入了新的脉冲条件,在不假设激励函数的可微性、单调性的条件下,得到了系统平衡点的存在性、唯一性及全局指数稳定性的充分条件和指数收敛速率,且所得结果改进了一些已知文献的结论.     

具有脉冲和无限时滞的模糊细胞神经网络的全局指数稳定性

In this paper,the global exponential stability of fuzzy cellular neural networks with impulses and infinite delays is investigated.Based on an impulsive delayed integro-differential inequality and the properties of fuzzy logic operation and M-matrix,an easily verified sufficient condition is obtained.Moreover,the exponential convergent rate for the fuzzy cellular neural networks with impulses and infinite delays is also given.An example is given to illustrate the effectiveness of our theoretical result.     

Global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms

In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.     

Dynamical behaviors of fuzzy cellular neural networks with discrete time delays

This paper is concerned with the dynamical behavior of fuzzy cellular neural networks with discrete time delays. By constructing proper Lyapunov functional, applying inequality technique and the homeomorphism theory, some new sufficient conditions checking the existence, uniqueness of the equilibrium point and its global exponential stability are obtained for fuzzy cellular neural networks. The results of this paper are new and they complement previously known results, Moreover an example is given to show the effectiveness of the obtained results.     

Global exponential stability of fuzzy cellular neural networks with delays and reaction–diffusion terms

In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction–diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction–diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits.     

Stationary oscillation for high-order Hopfield neural networks with time delays and impulses

We investigate stationary oscillation for high-order Hopfield neural networks with time delays and impulses. In a recent paper [J. Zhang, Z. J. Gui, Existence and stability of periodic solutions of high-order Hopfield neural networks with impulses and delays, Journal of Computational and Applied Mathematics 224 (2008) 602-613], the authors claim that they obtain a criterion of existence, uniqueness, and global exponential stability of periodic solution (i.e. stationary oscillation) for high-order Hopfield neural networks with time delays and impulses. In this paper, we point out that the main result of the recent paper is unture, and present a new sufficient condition of stationary oscillation for the neural networks. A numerical example is given to illustrate the effectiveness of the obtained result.     

Analysis of global exponential stability and periodic solutions of FCNNs with constant delays and time-varying delays

In this paper, we investigate a class of fuzzy cellular neural networks with constant delays and time-varying delays. By constructing suitable Lyapunov functional and employing Young inequality, we find sufficient conditions for the existence, uniqueness, global exponential stability of equilibrium, and the existence of periodic solutions of fuzzy cellular neural networks with time-varying delays. The results of this paper are new and they extend previously known results.     

Exponential stability and periodic solution for fuzzy BAM Neural networks with time varying delays

In this paper, a class of fuzzy BAM neural networks with time varying delays is discussed. By using the properties of M-matrix, Linear Matrix Inequality（LMI） approach and general Lyapunov-Krasovskii functional, some new sufficient conditions are derived to ensure the existence of periodic solutions and the global exponential stability of the fuzzy BAM neural networks with time varying delays. These results have important significance in the design of global exponential stable BAM networks with delays. Moreover, an example is given to illustrate that the conditions of the results in the paper are feasible.     

具传输时滞和变系数的模糊细胞神经网络的指数稳定性及周期解

介绍一类具传输时滞和变系数的模糊细胞神经网络(FCNN),通过使用线性矩阵不等式(LMI)和Lyapunov-Krasovskii泛函,研究它的周期解的存在性及全局指数稳定性,并获得一些充分条件。此外,给出一个实例说明结果是可行的。     

随机变时滞模糊神经网络的均方指数稳定性

利用Lyapunov泛函和随机分析的方法,研究了一类具有变时滞随机模糊细胞神经网络的均方指数稳定性,得到了这类神经网络均方指数稳定性的充分条件.数值例子说明了得到的结果的有效性.