具比例时滞高阶广义细胞神经网络的全局指数周期性

研究了一类具比例时滞高阶广义细胞神经网络的全局指数周期性与稳定性问题.通过非线性变换y_i(t)=x_i(e~t),将一类具比例时滞高阶广义细胞神经网络等价地变换成一类具常时滞与时变系数的高阶广义细胞神经网络,再通过构造合适的Lyapunov泛函,利用Brouwer压缩映象原理和一些不等式的分析技巧,得到了该神经网络的周期解的存在唯一且全局指数周期与全局指数稳定的几个新的时滞依赖的充分条件,并且这些条件仅通过一些简单的代数运算就容易验证.     

具有可变时滞的Hopfield型随机神经网络的指数稳定性

研究了具有可变时滞的Ｈｏｐｆｉｅｌｄ型随机种经网络的指数稳定性,应用Ｒａｚｕｍｉｋｈｉｎ定理与 Ｌｙａｐｕｎｏｖ函数,建立了这种神经网络的均方指数稳定与几乎必然指数稳定的两类判据,一类是时 滞相关而另一类是时滞无关．     

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.     

Caputo分数阶时滞细胞神经网络的稳定性分析

研究了一类Caputo分数阶时滞细胞神经网络模型的稳定性.通过利用分数阶微积分中的常数变分法,得到了Caputo分数阶时滞细胞神经网络解的差分形式,推导出模型的有界解和平衡点的存在性与唯一性,最后证明了神经网络的全局指数稳定性.     

变时滞细胞神经网络的指数稳定的一个充分条件

本文我们研究了变时滞细胞神经网络的指数稳定性。利用分析技巧，获得了变时滞细胞神经网络的指数稳定的充分条件，改进了已有文献中的相应结果。     

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.     

变时滞BAM神经网络的全局指数稳定性

研究一类变时滞BAM神经网络平衡点的全局指数稳定性问题.在不要求激励函数全局Lipschitz条件下,通过构造合适的Lyapunov泛函,并结合Young不等式,得到了BAM神经网络模型在一定条件下全局指数稳定的一些充分条件,推广和改进了前人的相关结论,为综合设计指数稳定的时滞BAM神经网络提供了依据.     

时滞细胞神经网络的时滞相关指数稳定性

应用Lyapunov泛函法研究了具有时滞的细胞神经网络（DCNNs）的平衡点的全局指数稳定性，获得了一个指数稳定性的判定准则。这个准则与时滞的大小有关，即DCNNs是指数稳定的只要系统所含时滞不超过一个界。     

多时滞随机神经网络的稳定性

通过构建李雅普偌夫函数的方法和利用半鞅收敛定理对一类随机时滞神经网络的全局指数稳定进行了分析,提出了易于判定随机时滞神经网络几乎必然指数稳定性新的代数判据,推广了[1]中的主要结论.     

具有时滞的细胞神经网络周期解与稳定性

本文研究了具有时滞的细胞神经网络周期解存在性和平凡解的稳定性问题 .利用 Lyapunov函数法并结合不等式分析技巧 ,我们首先证明了时滞细胞神经网络的解是有界的 ,然后建立了时滞细胞神经网络的周期解的存在准则 ,最后在时滞细胞神经网络有平衡点时 ,给出了神经网络系统的平衡点指数稳定的充分条件 .其结果推广了文 [7,8]的相应结果 .     

具有时滞的细胞神经网络的全局指数稳定性

利用Liapunov函数方法,结合积分不等式技巧,分析了时滞细胞神经网络的平衡点存在的唯一性和全局指数稳定性,保证时滞细胞神经网络全局指数稳定的一个新的充分判据被得到.所得判据比已有文献具有更少的限制,为实际应用提供了方便.     

Exponential synchronization of stochastic fuzzy cellular neural networks with time delay in the leakage term and reaction-diffusion

Separate studies have been published on the stability of fuzzy cellular neural networks with time delay in the leakage term and synchronization issue of coupled chaotic neural networks with stochastic perturbation and reaction-diffusion effects. However, there have not been studies that integrate the two fields. Motivated by the achievements from both fields, this paper considers the exponential synchronization problem of coupled chaotic fuzzy cellular neural networks with stochastic noise perturbation, time delay in the leakage term and reaction-diffusion effects using linear feedback control. Lyapunov stability theory combining with stochastic analysis approaches are employed to derive sufficient criteria ensuring the coupled chaotic fuzzy neural networks to be exponentially synchronized. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.     

Delay-dependent exponential stability of cellular neural networks with time-varying delays

The global exponential stability of cellular neural networks (CNNs) with time-varying delays is analyzed. Two new sufficient conditions ensuring global exponential stability for delayed CNNs are obtained. The conditions presented here are related to the size of delay. The stability results improve the earlier publications. Two examples are given to demonstrate 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.     

Improved global exponential stability criteria of cellular neural networks with time-varying delays

In this paper, the existence and uniqueness of the equilibrium point and stability of the cellular neural networks (CNNs) with time-varying delays are analyzed and proved. Several global exponential stability conditions of the neural networks are obtained by the delay differential inequality and matrix measures approach. The obtained results are extensions of the earlier literature. The approach used in this paper is also suitable for delayed Hopfield neural networks and delayed bi-directional associative memory neural networks whose activation functions are often nondifferentiable or unbounded. Two simulation examples in comparison to previous results in literature are shown to check the theory in this paper.     

Exponential stability of delayed fuzzy cellular neural networks with diffusion

The exponential stability of delayed fuzzy cellular neural networks (FCNN) with diffusion is investigated. Exponential stability, significant for applications of neural networks, is obtained under conditions that are easily verified by a new approach. Earlier results on the exponential stability of FCNN with time-dependent delay, a special case of the model studied in this paper, are improved without using the time-varying term condition: dτ(t)/dt < μ.     

Stability of delayed cellular neural networks

Global asymptotic stability and exponential stability of delayed cellular neural networks is considered in this paper. Based on the Lyapunov stability theorem as well as a fact about the elemental inequality, some new sufficient conditions are given for global asymptotic stability and exponential stability of delayed cellular neural networks. The results are less conservative than those established in the earlier references. Three examples are given to illustrate the applicability of these conditions.     

Existence and global exponential stability of periodic solution of CNNs with impulses

Sufficient conditions are obtained for the existence and global exponential stability of a unique periodic solution of cellular neural networks with variable time delays and impulses by using Mawhin’s continuation theorem of coincidence degree and by means of a method based on delay differential inequality.