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.     

在时间尺度上研究中立型泄漏时滞BAM神经网络的概周期解

在时间尺度上,通过使用线性动力方程的指数二分法、不动点理论和微积分理论,研究中立型泄漏时滞BAM神经网络模型,获得了一些使中立型泄漏时滞BAM神经网络模型概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.     

时间尺度上漏项中具有变时滞的中立型模糊BAM神经网络的概周期解

本文研究了一类时间尺度上漏项中具有变时滞的中立型模糊BAM神经网络.利用时间尺度上线性动力学方程的指数二分法,Banach不动点定理和时间尺度上的微积分理论,得到了这类模糊BAM神经网络在时间尺度上概周期解的存在性和指数稳定性的充分条件.     

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.     

连续型BAM神经网络的指数稳定性

首先将连续型双向联想记忆神经网络转化成一个特殊的Hopfield网络模型.在此基础上,对连续BAM神经网络的指数稳定性进行了新的分析,证明了神经网络连接权矩阵在给定的约束条件下有唯一平衡点.所做的分析可以用于设计全局指数稳定的神经网络.     

具分布时滞神经网络周期解的指数稳定性

本文利用一些分析技巧,获得了具分布时滞的双向联想记忆（BAM）神经网络模型周期解的指数稳定性的结论。     

具有时滞的扩散BAM神经网络的指数稳定性

研究一类具有反应扩散的滞后BAM神经网络平衡点的存在性唯一性和全局指数稳定性.运用拓扑同胚映射,Lyapunov泛函以及多参数方法,得到关于平衡点存在唯一性和全局指数稳定性的充分条件,将相关文献的结果推广到正整数r范数上.     

时间尺度上时滞Cohen-Grossberg BAM神经网络系统概周期解的全局指数稳定性

本文通过使用李雅谱诺夫函数和不等式技巧等,在时间尺度上研究时滞CohenGrossberg BAM神经网络系统概周期解的全局指数稳定性,在此,不需要假设反应函数的有界性.最后,获得一些使其存在全局指数稳定的概周期解的充分条件,并给出例子去验证结果的有效性.     

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.     

中立型驱动反应BAM 神经网络的全局渐近同步性

针对一类具有时滞的中立型驱动反应BAM神经网络,提出全局渐近同步性问题.不使用现有文献中传统的李雅普诺夫泛函、矩阵测度和线性矩阵不等式(LMI)等已被广泛应用于研究神经网络全局渐近同步性的方法,而是通过构造2个微分不等式U 1(t)和U 1(t),利用微分不等式方程和不等式技巧解出2个不等式,得到能够确保中立型驱动反应BAM神经网络全局渐近同步的两个新的充分条件.     

EXISTENCE AND STABILITY OF PERIODIC SOLUTIONS OF DELAYED BAM NEURAL NETWORKS WITH PERIODIC COEFFICIENTS

In this paper, we study the BAM neural networks with variable coefficients and delays. By using the Banach fixed point theorem and constructing suitable Lyapunov function, we obtain some sufficient conditions ensuring the existence, uniqueness and global stability of periodic solution. These results are helpful to design global exponential stable BAM networks and periodic oscillatory BAM networks.     

The Existence and Global $p$-Exponential Stability of Periodic Solution for Stochastic BAM Neural Networks with Delays

In this paper, we consider a class of stochastic BAM neural networks with delays. By establishing new integral inequalities and using the properties of spectral radius of nonnegative matrix, some sufficient conditions for the existence and global $p$-exponential stability of periodic solution for stochastic BAM neural networks with delays are given. An example is provided to show the effectiveness of the theoretical results.     

Convergence behavior of delayed bidirectional associative memory cellular neural networks with asymptotically periodic coefficients

This paper considers the convergence behavior of delayed BAM cellular neural networks with asymptotically periodic coefficients. By applying mathematical analysis techniques, some new sufficient conditions are obtained to ensure that solutions of the networks converge to a periodic function.     

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.     

PERIODIC SOLUTIONS AND STABILITY ANALYSIS FOR NEURAL NETWORKS WITH TIME-VARYING DELAY

1 IntroductionIt is well known that both in biological and man-made neural systems, inte-gl.ation afld communication delays are ubiquitous, and often become sources ofinstabilitY' The de1ays in electronic neural networks are usually time varying,and sometimes vary vio1ently with time due to the finite switching speed ofamplifiers and faults in the electrical circuit. They s1ow down the transmissionrate and tend to introduce some degree of instability in circuits. Therefore,fast response must …     

BAM-type impulsive neural networks with time-varying delays

By using the continuation theorem of coincidence degree theory and constructing a suitable Lyapunov functional, we derive some sufficient conditions for the existence and global exponential stability of a unique periodic solution of BAM neural networks, which assumes neither the monotony nor the boundedness of the activation functions. It is believed that these results are significant and useful for the design and applications of BAM neural networks.     

A new criterion to global exponential periodicity for discrete-time BAM neural network with infinite delays

The discrete-time bidirectional associative memory neural network with periodic coefficients and infinite delays is studied. And not by employing the continuation theorem of coincidence degree theory as other literatures, but by constructing suitable Liapunov function, using fixed point theorem and some analysis techniques, a sufficient criterion is obtained which ensures the existence and global exponential stability of periodic solution for the type of discrete-time BAM neural network. The obtained result is less restrictive to the BAM neural networks than previously known criteria. Furthermore, it can be applied to the BAM neural network which signal transfer functions are neither bounded nor differentiable. In addition, an example and its numerical simulation are given to illustrate the effectiveness of the obtained result.     

PERIODIC SOLUTIONS OF BAM NETWORKS WITH STATE DEPENDENT DELAYS

We use Krasnosel'skii's fixed point theorem to show that the following BAM networks with state dependent delays has a periodic solution.     

Functional differential inclusions and dynamic behaviors for memristor-based BAM neural networks with time-varying delays

In this paper, we formulate and investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, the viability and dissipativity of solutions for functional differential inclusions and memristive BAM neural networks can be guaranteed by the matrix measure approach and generalized Halanay inequalities. Then, a new method involving the application of set-valued version of Krasnoselskii’ fixed point theorem in a cone is successfully employed to derive the existence of the positive periodic solution. The dynamic analysis in this paper utilizes the theory of set-valued maps and functional differential equations with discontinuous right-hand sides of Filippov type. The obtained results extend and improve some previous works on conventional BAM neural networks. Finally, numerical examples are given to demonstrate the theoretical results via computer simulations.     

Dynamical analysis of fuzzy BAM neural networks with variable delays

In this work, employing Lyapunov functional and elemental inequality ($2ab \leqslant ra^2 + \tfrac{1} {r}b^2$2ab \leqslant ra^2 + \tfrac{1} {r}b^2, r > 0), some sufficient conditions are derived for the existence and uniqueness of periodic solution of fuzzy bidirectional associated memory (BAM) neural networks with variable delays. Some new and simple criteria are obtained to ensure global exponential stability of periodic solution, which are important in design and applications of fuzzy BAM neural networks.