不确定时变时滞连续Hopfield神经网络的鲁棒稳定性

本文研究了时变时滞连续Hopfield神经网络存在有界参数不确定时的稳定性问题,提出了保证该网络鲁棒稳定的代数Riccati方程(ARE)设计算法.而忽略时滞或忽略参数不确定,则得到有别于以往结果的各种有关参数不确定连续Hopfield神经网络稳定性的定理.     

时滞线性区间系统的鲁棒稳定与鲁棒镇定

利用一个微分不等式给出了几类具有区间 系数的时滞系统的鲁棒稳定和鲁棒镇定的充分条件,这些条件只需判断一个常数矩阵是否为 M矩阵,使用方便．     

基于增广泛函的混合时滞区间神经网络的鲁棒稳定性

本文针对一类带有混合时变时滞 (离散和分布时滞)的区间递归神经网络进行了全局鲁棒稳定性研究.与之前的处理方法不同,在本文中通过使用一种新型的增广Lyapunov-Krasovskii泛函,从而得到了一类新颖的关于区间递归神经网络的时滞依赖全局鲁棒稳定性判据.在新的增广泛函中,由于首次使用了带有激活函数的积分项,系统状态和激活函数之间的关系将被更好地表示出来.因此,本文提出的判据具有更小的保守性.同时,在本文提出的判据中,放松了时变时滞变化率必须小于1的限制.仿真结果进一步证明了本文结果的有效性.     

具有时滞的线性区间系统的鲁棒稳定性

本文给出了线性区间时滞系统鲁棒稳定性的一些结果，这些结果推广和是了前人关于线性时滞系统鲁棒稳定性的相关结论，同时还讨论了线性区间时滞系统的稳定度，最后讨论了线性区间时滞大系统的鲁棒稳定性。     

基于划分时滞区间的一类Cohen-Grossberg神经网络的鲁棒稳定性

研究一类具有时变时滞及参数不确性的Cohen-Grossberg神经网络的鲁棒稳定性问题.应用划分时滞区间的思想构造了一个新的Lyapunov泛函,并以线性矩阵不等式的形式给出了平衡点全局鲁棒稳定性判据,新判据放松了时变时滞变化率必须小于1的限制.仿真结果进一步证明了所得结论的有效性.

     

不确定时滞BAM神经网络的鲁棒稳定性

利用自由权值矩阵和不等式分析技巧,研究了一类不确定时滞BAM神经网络的鲁棒稳定性问题。通过构造适当的Lyapunov泛函,对于所有允许的不确定性,以线性矩阵不等式形式给出了时滞BAM神经网络的全局鲁棒稳定性判据,该判据能够利用Matlab的LMI工具箱很容易地进行检验。此外,仿真示例进一步证明了判据的有效性。     

时变时滞神经网络系统的时滞依赖的鲁棒稳定性准则

基于Lyapunov稳定性理论和线性矩阵不等式技术,针对一类带有范数有界不确定性的时变时滞神经网络系统,给出时滞依赖的鲁棒稳定性准则.系统稳定的充分条件是在激励函数满足一类更为通用的条件下得到的,即激励函数不必是单调可微的,并且消除对时变时滞导数的限制.所给的准则可用Matlab中的线性矩阵不等式控制工具箱进行验证.仿真结果进一步证明结论的有效性.     

具有时滞的线性区间动力系统的鲁棒稳定性

利用Lyapunov原理,给出了具有时滞的线性区间动力系统鲁棒稳定的一些充分条件,推广和改进了前人关于具有时滞的线性区间动力系统的鲁棒稳定性的相关结论,并举例说明了本文结果不仅保守性小,且计算简单.     

具有时滞的线性区间系统的鲁棒稳定性

本文给出了线性区间时滞系统鲁棒稳定性的一些结果，这些结果推广和改进了前人关于线性时滞系统鲁棒稳定性的相关结论，同时还讨论了线性区间时滞系统的稳定度，最后讨论了线性区间时滞大系统的鲁棒稳定性．     

一类不确定时滞系统的时滞依赖鲁棒可靠镇定

主要研究了一类状态时滞不确定性线性系统的时滞依赖鲁棒可靠镇定问题,系统状态矩阵和时滞状态矩阵中在着范数有界的时变参数不确定性,故障执行器集合是执行器集合的子集,采用双积分型Lyapunov函数,最终将问题归结为求解LMI,最后给出一个数值例子验证方法的可行性。     

一类高阶Cohen-Grossberg神经网络的多周期性分析

利用状态空间分解方法,探讨一类具有特殊激励函数的高阶Ｃｏｈｅｎ-Ｇｒｏｓｓｂｅｒｇ神经网络的多周期性问题．该类神经网络的激励函数包括带有饱和区的非递减函数以及一般的细胞神经网络激励函数等．给出了保证此类网络的周期环在饱和区内局部指数收敛的充分条件．所得结果表明,一个狀维网络可以有２ⁿ个局部指数收敛的周期环存在于饱和区．最后以一个数值例子说明了所得结果的有效性．     

Robust stability for interval Hopfield neural networks with time delay.

The conventional Hopfield neural network with time delay is intervalized to consider the bounded effect of deviation of network parameters and perturbations yielding a novel interval dynamic Hopfield neural network (IDHNN) model. A sufficient condition related to the existence of unique equilibrium point and its robust stability is derived.     

Analysis for Cohen-Grossberg neural networks with multiple delays

The stability analysis of Cohen-Grossberg neural networks with multiple delays is given. An approach combining the Lyapunov functional with the linear matrix inequality （LMI） is taken to obtain the sufficient conditions for the globally asymptotic stability of equilibrium point. By using the properties of matrix norm, a practical corollary is derived. All results are established without assuming the differentiability and monotonicity of activation functions. The simulation samples have proved the effectiveness of the conclusions.     

Existence of periodic solutions for a class of Cohen-Grossberg type neural networks with neutral delays

In this short communication Cohen-Grossberg neural network with neutral delays is studied by means of an abstract theorem of k-set contractive operator and some analysis techniques. Sufficient conditions are obtained for the existence of periodic solutions.     

Exponential stability analysis of stochastic reaction-diffusion Cohen-Grossberg neural networks with mixed delays

In this paper, we study a new class of stochastic Cohen-Grossberg neural networks with reaction-diffusion and mixed delays. Without the aid of nonnegative semimartingale convergence theorem, the method of variation parameter and linear matrix inequalities technique, a set of novel sufficient conditions on the exponential stability for the considered system is obtained by utilizing a new Lyapunov-Krasovskii functional, the Poincaré inequality and stochastic analysis theory. The obtained results show that the reaction-diffusion term does contribute to the exponentially stabilization of the considered system. Therefore, our results generalize and improve some earlier publications. Moreover, two numerical examples are given to show the effectiveness of the theoretical results and demonstrate that the stability criteria existed in the earlier literature fail.     

Analysis for Cohen-Grossberg neural networks with multiple delays

The stability analysis of Cohen-Grossberg neural networks with multiple delays is given. An approach combining the Lyapunov functional with the linear matrix inequality (LMI) is taken to obtain the sufficient conditions for the globally asymptotic stability of equilibrium point. By using the properties of matrix norm, a practical corollary is derived. All results are established without assuming the differentiability and monotonicity of activation functions. The simulation samples have proved the effectiveness of the conclusions.     

Color image associative memory on a class of Cohen-Grossberg networks

In this paper, neural associative memories for storing gray-scale and true color images are presented based on a class of reduced Cohen-Grossberg neural networks. Some fundamental conditions for endowing the networks with retrieval properties are proposed. Moreover, a system designing procedure is developed by using matrix decomposition. Numerical simulations show that the constructed networks can act as reliable noise-reducing systems for storing and retrieving color images.     

连续分布时滞Cohen-Grossberg神经网络渐近稳定性准则

通过选取一个新颖的Lyapunov-Krasovskii泛函和引入所考虑系统的等价描述系统, 讨论了具有时变和连续分布时滞Cohen-Grossberg神经网络的渐近稳定性问题. 在变时滞导函数有上界时, 给出能判定系统是渐近稳定时滞相关的充分性条件. 该条件以线性矩阵不等式形式给出, 易于用MATLAB工具箱LMI进行检验. 最后, 数值例子说明了所得结论的有效性.     

Robust global exponential stability of Cohen-Grossberg neural networks with time delays

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.