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。     

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．     

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．     

压缩感知邻域中的带限制性等距常数（英文）

众所周知,带限制性等距常数是压缩感知领域中的核心概念.在压缩感知理论发展的十几年历史中,几乎所有的重要理论结果都与这个概念密切相关.此文主要是总结近十余年来带限制性等距常数的若干重要结果,特别是最佳上界的发现.我们首先表明许多具有最少行数的随机矩阵满足这个性质,而一些确定性矩阵也满足这个性质.但是与随机矩阵相比,确定性矩阵的行数要明显多.其次,我们给出了刻画l_1优化模型范数最小解与最稀疏解等价性的最佳带限制性等距常数,对于l_p(0p 1)优化模型也得到了类似结果.最后,我们延拓这些结果到低秩矩阵恢复以及在字典表示下具有稀疏信号恢复的情形.     

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．     

非线性抛物组非均匀网格差分解的唯一性和稳定性

１．引言 １．对一维非线性抛物组,在文献山中已构造一般非均匀网格差分格式,其中差分逼近的组合系数对不同的网格点和不同的网格层可以不同,并且运用不动点原理证明了差分解的存在性和收敛性．在非均匀网格差分格式中差分逼近的组合系数为常数的情形,文献［２］证明了具有有界二阶差商的离散向量解的存在性、唯一性和稳定性．本文将对文献［１］中构造的一般非均匀网格差分格式,证明所得到的差分解的唯一性和稳定性． 考虑如下非线性抛物组其中是未知的ｍ－维向量函数是给定的矩阵函数,ｊ（ｘ,ｔ,ｕ,ｐ）。是给定的ｍ－维向量函数…     

具有矩阵伸缩的尺度函数双正交特征

考虑了高维具有矩阵伸缩的尺度函数双正交性问题．给出了这种情形下尺度函数双正交性的三个特征刻划及尺度函数属于Ｌ～２（Ｒ～ｄ）的几个充分条件．     

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．     

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．     

随机环境中多维分枝链的增长率及灭绝概率

本文在随机环境中马氏链的框架下研究随机环境中多维分枝链(简记MBCRE)的极限性质,获得了MBCRE的母函数的一些性质,利用这些性质和随机矩阵乘积的弱收敛性证明了MBCRE中的条件均方收敛性与a．s．收敛性以及灭绝概率的估算等．这些结果是对Athrey与Karlin(1972)和Cohn(1989)的极限定理的推广．     

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.