生存轨道与集值映象的不动点

基于无穷维空间中的生存定理，我们研究了集值映象的不动点，得到了一个新的不动点定理，推广和改进了[1]和[5]中的相应结果。     

广义分解定理与两个不等式

本文给出了Banach空间广义分解定理的一个初等证明,并利用它来证明两个对称不等式.这是首次在Banach空间获得这样的不等式.     

Steiner定理的拓广

Steiner定理是一个著名的几何题,它的证明更是给广大数学爱好者予启发和想象.本文给出Steiner定理的拓广,供大家参考.Steiner定理在△ABC中,∠B和∠C的平分线BD与CE相等,则AB=AC.拓广定理(如图1)在△ABC中,设BD、CE分别为∠ABC和∠ACB的n≥2等分角线中的任意两条相应的分角线段     

密度泛函估计的重对数律，中心极限定理和不变原理

设X_1,…,X_n是从分布密度为f(单变量实值函数)的总体中抽出的iid.样本.μ=EX_1。本文研究了密度泛函θ=f(μ)的核型估计为通常的Rosenblatt-Parzen核估计)的大样本性质。     

代数体函数的定理

本文定义并研究了代数体函数的加法．结合杨乐的方法,将仪洪勋联系重值的亚纯函数唯一性定理推广到多值的代数体函数．     

~ Transform Demonstration of Dark Soliton Solutions Found by Inverse Scattering

One of the basic problems about the inverse scattering transform for solving a completely integrable nonlinear evolutions equation is to demonstrate that the Jost solutions obtained from the inverse scattering equations of Cauchy integral satisfy the Lax equations. Such a basic problem still exists in the procedure of deriving the dark soliton solutions of the NLS equation in normal dispersion with non-vanishing boundary conditions through the inverse scattering transform. In this paper, a pair of Jost solutions with same analytic properties are composed to be a 2 × 2 matrix and then another pair are introduced to be its right inverse confirmed by the Liouville theorem. As they are both 2 × 2 matrices, the right inverse should be the left inverse too, based upon which it is not difficult to show that these Jost solutions satisfy both the first and second Lax equations. As a result of compatibility condition, the dark soliton solutions definitely satisfy the NLS equation in normal dispersion with non-vanishing boundary conditions.     

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.     

Hilbert Space of Probability Density Functions Based on Aitchison Geometry

The set of probability functions is a convex subset of L1 and it does not have a linear space structure when using ordinary sum and multiplication by real constants. Moreover, difficulties arise when dealing with distances between densities. The crucial point is that usual distances are not invariant under relevant transformations of densities. To overcome these limitations, Aitchison＇s ideas on compositional data analysis are used, generalizing perturbation and power transformation, as well as the Aitchison inner product, to operations on probability density functions with support on a finite interval. With these operations at hand, it is shown that the set of bounded probability density functions on finite intervals is a pre-Hilbert space. A Hilbert space of densities, whose logarithm is square-integrable, is obtained as the natural completion of the pre-Hilbert space.