半线性抛物型方程组Cauchy问题的一点注记

该文讨论Ｃａｕｃｈｒ问题整体光滑解的存在性，唯一性与渐近性，推广了文［２，３，１１，６，７，８，９］中相应的结果．这里ｕ＝（ｕ１，…，ｕｎ）Ｔ，Ａｉ（ｕ）（ｉ＝１，２，…，Ｎ）为ｎ×ｎ矩阵值函数，Ｄ为可对角化的ｎ×ｎ常数矩阵且其特征根大于０．     

多目标半定规划的互补弱鞍点和G-鞍点最优性条件

对于含矩阵函数半定约束和多个目标函数的多目标半定规划问题，给出Lagrange函数在弱有效意义下的互补弱鞍点和Geofrrion恰当有效意义下的G-鞍点的定义及其等价定义．然后，在较弱的凸性条件下，利用含矩阵和向量约束的择一性定理，建立多目标半定规划的互补弱鞍点和G-鞍点充分必要条件．     

局部凸空间的正规性

本文建立局部凸拓扑向量空间的正规性、完全正规性和单调正规性的等价条件．作为这些结果的应用，研究了函数空间的单调正规性和可度量性．     

Lipschitz常数缩减的散乱数据插值

在计算机辅助设计几何中，变差缩减是一个非常重要的概念，本文分析了函数的变差和Ｌｉｐｓｃｈｉｔｚ常数的关系，指出可以用Ｌｉｐｓｃｈｉｔｚ常数来控制变差，由于变差的概念只限于一维的情形，而Ｌｉｐｓｃｈｉｔｚ常数适用于任意维，这样在高维时就可用Ｌｉｐｓｃｈｉｔｚ常数缩减的概念来代替变差缩减的概念，文中构造性地证明了Ｌｉｐｓｃｈｉｔｚ常数缩减的散乱数据插值函数的存在性，并且对这类函数的性质及光滑性条件进行了讨论．     

平面一阶非线性椭圆型方程组的复合边值问题

本文研究平面一阶非线性椭圆型方程的复合边值问题，其中函数Ｈ关于后两个变量李普希兹连续，且关于最后一个变量的李普希兹常数严格小于一（椭圆性条件），而函数ｆ满足形如的自然增长条件．     

一类具有对称与中心对称性的有理函数双边留数插值问题

获得了在Sl^(s)[a,b]函数类中具有对称与中心对称性质的矩阵值两边留数插值问题的可解性条件。给出该问题所有解的一个线性分式变换表达形式．所用的方法是构造相同的矩阵函数作为线性分式变换的系数矩阵。     

关于双特征Beltrami方程

该文研究空间Ｂｅｌｔｒａｍｉ方程的推广形式，即双特征Ｂｅｌｔｒａｍｉ方程．利用外微分形式与矩阵的外代数等工具，将双特征Ｂｅｌｔｒａｍｉ方程转化为一个非齐次的狆 调和方程，转化过程中只用到加于特征矩阵的一致椭圆型条件．然后验证了算子犃满足的条件：Ｌｉｐｓｃｈｉｔｚ型条件、单调不等式、齐次性条件以及算子犅满足的控制增长条件．并利用得到的狆 调和方程，给出了双特征Ｂｅｌｔｒａｍｉ方程广义解分量函数的弱单调性结果．     

球面导数，高阶导数与正现族

本文应用陈怀惠和顾永兴关于Ｚａｌｃｍａｎ不正规性条件的改进结果，推广和加强了Ｌａｐｐａｎ的一个正规定则．Ｌａｐｐａｎ证明：若亚纯函数族中的所有函数的球面导数的幂方（大于２）在紧集上的积分一致有界，则该族是正则的．本文证明，把积分限制在函数值的模小于给定常数的子集上，结论仍然成立．同时，用高阶导数的积分替代球面导数的积分，得到十分一般的结果．另外对幂方为２的情形也进行了讨论．     

增长曲线模型中UMRE估计的存在性

对于设计矩阵不满秩，协方差阵任意或具有均匀结构或序列结构的正态增长曲线模型，本文讨论参数矩阵的一致最小风险同变（UMng）估计的存在性．在仿射变换群GI和转移交换群、二次损失和矩阵损失下本文分别获得存在回归系数矩阵的线性可估函数矩阵的UMRE估计的充要条件，推广了由[21]给出的在设计矩阵满秩下估计回归系数矩阵的结果．本文还首次证明了在群G1和二次损失下不存在协方差阵V和trV的UMRE估计．     

增长曲线模型中一致最小风险无偏估计的存在性

考虑协方差阵任意，或具有均匀协方差结构，或具有序列协方差结构的正态增长曲线模型本文将文[19]在设计矩阵满秩，且仅估计回归系数矩阵的情形获得的结果推广到设计矩阵不必列满秩，且同时估计回归系数矩阵的线性可估函数和协方差阵（或有关参数）的情形；在凸损失函数类和矩阵损失函数下，给出存在一致最小风险无偏估计的充分必要条件．     

Global exponential stability in Lagrange sense for recurrent neural networks with both time-varying delays and general activation functions via LMI approach

In this paper, we study the global exponential stability in a Lagrange sense for recurrent neural networks with both time-varying delays and general activation functions. Based on assuming that the activation functions are neither bounded nor monotonous or differentiable, several algebraic criterions in linear matrix inequality form for the global exponential stability in a Lagrange sense of the neural networks are obtained by virtue of Lyapunov functions and Halanay delay differential inequality. Meanwhile, the estimations of the globally exponentially attractive sets are given out. The results derived here are more general than that of the existing reference. Finally, two examples are given and analyzed to demonstrate our results.     

Global output convergence of recurrent neural networks with distributed delays

This paper discusses the global output convergence of a class of recurrent neural networks with distributed delays. The inputs of the neural networks are required to be time varying and the activation functions to be globally continuous and monotone nondecreasing. By using the definiteness of matrix and the properties of M-matrix, several sufficient conditions are established to guarantee the global output convergence of this class of neural networks. Symmetry in the connection weight matrices and the boundedness of the activation functions are not required in this paper. The convergence results are useful in solving some optimization problems and in the design of recurrent neural networks with distributed delays.     

Global Exponential Stability in Lagrange Sense for Delayed Memristive Neural Networks with Parameter Uncertainties

This paper addresses the Lagrange stability of memristive neural networks with leakage delay and time-varying transmission delays as well as parameter uncertainties. Based on the theory of Filippov''s solution, by using Lyapunov-Krasovskii functionals and the free-weighting matrix method, sufficient conditions in terms of linear matrix inequality (LMI) are given to ascertain the networks with different kinds of activation functions to be stable in Lagrange sense. Meanwhile the estimation of globally attractive sets are given. Finally, numerical simulations are carried out to illustrate the effectiveness of theoretical results.     

Estimation of exponential convergence rate and exponential stability for neural networks with time-varying delay

We study the problem of estimating the exponential convergence rate and exponential stability for neural networks with time-varying delay. Some criteria for exponential stability are derived by using the linear matrix inequality (LMI) approach. They are less conservative than the existing ones. Some analytical methods are employed to investigate the bounds on the interconnection matrix and activation functions so that the systems are exponentially stable.     

具有扩散影响的Hopfield型神经网络的全局渐近稳定性

对具有扩散影响的Hopfield型神经网络平衡点的存在唯一性和全局渐近稳定性进行了研究.在激活函数单调非减、可微且关联矩阵和Liapunov对角稳定矩阵有关时,利用拓扑度理论得到了系统平衡点存在的充分条件.通过构造适当的平均Liapunov函数,分析了系统平衡点的全局渐近稳定性.所得结论表明系统的平衡点(如果存在)是全局渐近稳定的而且也蕴含着系统的平衡点的唯一性.     

Exponential stability of Cohen–Grossberg neural networks with delays

The exponential stability characteristics of the Cohen–Grossberg neural networks with discrete delays are studied in this paper, without assuming the symmetry of connection matrix as well as the monotonicity and differentiability of the activation functions and the self-signal functions. By constructing suitable Lyapunov functionals, the delay-independent sufficient conditions for the networks converge exponentially towards the equilibrium associated with the constant input are obtained. By employing Halanay-type inequalities, some sufficient conditions for the networks to be globally exponentially stable are also derived. It is not doubt that our results are significant and useful for the design and applications of the Cohen–Grossberg neural networks.     

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.     

Existence and global asymptotic stability of periodic solutions for Hopfield neural networks with discontinuous activations

In this paper, we study a class of neural networks with discontinuous activations, which include bidirectional associative memory networks and cellular networks as its special cases. By the Leray–Schauder alternative theorem, matrix theory and generalized Lyapunov approach, we obtain some sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of the periodic solution. Our results are less restrictive than previously known criteria and can be applied to neural networks with a broad range of activation functions assuming neither boundedness nor monotonicity.     

Global exponential stability for a class of retarded functional differential equations with applications in neural networks

In this paper, the global exponential stability and asymptotic stability of retarded functional differential equations with multiple time-varying delays are studied by employing several Lyapunov functionals. A number of sufficient conditions for these types of stability are presented. Our results show that these conditions are milder and more general than previously known criteria, and can be applied to neural networks with a broad range of activation functions assuming neither differentiability nor strict monotonicity. Furthermore, the results obtained for neural networks with time-varying delays do not assume symmetry of the connection matrix.     

Delay-dependent stochastic stability of delayed Hopfield neural networks with Markovian jump parameters

In this paper, the problem of stochastic stability for a class of time-delay Hopfield neural networks with Markovian jump parameters is investigated. The jumping parameters are modeled as a continuous-time, discrete-state Markov process. Without assuming the boundedness, monotonicity and differentiability of the activation functions, some results for delay-dependent stochastic stability criteria for the Markovian jumping Hopfield neural networks (MJDHNNs) with time-delay are developed. We establish that the sufficient conditions can be essentially solved in terms of linear matrix inequalities.