GLMS系列算法和时变信号模型的自适应处理

提出了具有指数数据窗的GLMS算法和近似GLMS算法.分析和计算证明,两算法不仅具有较高的起始收敛速度,而且还有较强的跟踪能力和小的失调量,适用于时变和非时变信号模型的自适应处理.     

关于Von Mises条件的注记

ＶｏｎＭｉｓｅｓ（１９３６）在一定条件下给出了独立同分布随机变量序列｛Ｘ＿ｎ｝之分布函数Ｆ（ｘ）属于三大吸引场的充分条件。受Ｇａｌａｍｂｏｓ（１９８７）文章的启发，在一定限制下，使ＶｏｎＭｉｓｅｓ条件也成为Ｆ∈Ｄ（Φ＿ａ）及Ｅ∈Ｄ（Φ＿ａ）的充要条件。     

贝叶斯型的时变系数回归模型

提出了具有时变系数的回归模型及其模型识别和参数估计的贝叶斯方 法．在此模型中对于时变回归系数的变动，应用了高斯型概率差分方程式作 为约束条件，称之为高斯型平滑性事先分布．模型中的超参数（ｈｙｐｅｒｐａｒａｍｅ－ ｔｅｒ）的估计，采用了最大似然估计法．模型的识别（差分次数的决定）应用了 Ａｋａｉｋｅ的最小 ＡＢＩＣ法．给出了模型估计的算法及其应用例子．最后，讨论了 平滑性事先分布中参数的最优估计的意义．     

适于带宽变化的链路共享方法

对于无线链路和共享信道的链路，由于受外界干扰或共享冲突的影响以及采取的应对措施，使得链路带宽处于变化中。这样的链路为不同用户所共享时，原有的分层链路共享方法由于假设带宽恒定，不能适用于这样的环境。通过对这一环境和链路共享机制深入分析。提出了适于带宽变化的链路共享方法。来满足用户对此链路带宽共享需求和服务质量需求。并通过大量仿真实验对这一方法进行了深入分析，验证了其可行性和合理性。     

Stability analysis of BAM neural networks with time-varying delays

Some new criteria for the global asymptotic stability of the equilibrium point for the bi-directional associative memory neural networks with time varying delays are presented. The obtained results present the structure of linear matrix inequality which can be solved efficiently. The comparison with some previously reported results in the literature demonstrates that the results in this paper provide one more set of criteria for determining the stability of the bi-directional associative memory neural networks with delays.     

非线性再生散度模型的广义变离差检验

研究了非线性再生散度模型的广义变离差检验,对于一类常见的正则散度分布族,解决了离差参数的齐性检验问题,得到了检验的似然比统计量和score统计量,并证明了score统计量的渐近χ2性.本文研究的非线性再生散度模型包括了许多常见的统计模型,诸如正态线性和非线性回归模型、广义线性模型和广义非线性模型等;同时,本文结果也适用于某些更复杂的分布,诸如基于极值分布、单纯形分布的非线性模型等.因此本文的结果进一步推广和发展了文献中已有的工作.     

Stability analysis of BAM neural networks with time-varying delays

Some new criteria for the global asymptotic stability of the equilibrium point for the bi-directional associative memory neural networks with time varying delays are presented. The obtained results present the structure of linear matrix inequality which can be solved efficiently. The comparison with some previously reported results in the literature demonstrates that the results in this paper provide one more set of criteria for determining the stability of the bi-directional associative memory neural networks with delays.     

A Large Deviation Principle for the Risk Process with Varying Premium

Let u ∈ R ,for any ω 〉 0, the processes X^ε = {X^ε（t）; 0 ≤ t≤ 1} are governed by the following random evolution equations dX^ε（t）= b（X^ε（t）,v（t））dt-εdSt/ε, where S={St; 0≤t≤1} is a compound Poisson process, the process v={v（t）; 0≤t≤1} is independent of S and takes values in R^m. We derive the large deviation principle for{（X^ε,v（.））; ε〉0} when ε↓0 by approximation method and contraction principle, which will be meaningful for us to find out the path property for the risk process of this type.     

极值类型定理的推广

把极值类型定理中的ｎ次方进一步推广成Ｕ（ｎ）次方，其中Ｕ（ｎ）是正则变化函数，它的指数大于零；同时通过一定的限制，得到相依序列随机样本的极值。