系统树中的近似Bayes方法

本文利用近似Ｂａｙｅｓ方法对一个具有树形结构的成败型系统的可靠性进行估计．本文证明了利用近似Ｂａｙｅｓ方法对系统的可靠性参数的估计，具点估计在Ｆｉｓｈｅｒ意义下是渐近有效的，其相应的置信下限也是Ｆｉｓｈｅｒ意义下渐近有效的．     

一种新的收敛加速概念：F－加速

本文介绍了一种新的收敛加速概念Ｆ－加速和一个判别函数Ｄ（ｎ），试图对序列收敛加速变换的讨论深入到定量分析，特别地对于在传统意义下不可加速的数列集合ＬＯＧ２，我们证明了它是可以Ｆ＿∞－加速的，在线性敛的情况下，我们给出了函数Ｄ（ｎ）的一个渐近估计式。     

一种新的收敛加速概念：F—加速

本文介绍了一种新的收敛加速概念Ｆ－加速和一个判别函数Ｄ（ｎ），试图对序列收敛加速变换的讨论深入到定量分析，特别地对于在传统意义下不可加速的数列集合ＬＯＧ２，我们证明了它是可以Ｆ∞－加速的，在线性敛的情况下，我们给出了函数Ｄ（ｎ）的一个渐近估计式。     

近似逼近l_1精确罚函数的罚函数

本文对可微非线性规划问题提出了一类新的近似渐近算法与一类渐近算法,它们都是基于一类逼近l1精确罚函数的罚函数而提出的.并证明了近似算法所得序列若有聚点则其为原问题的最优解;若所得序列为无界的,则给出了序列值收敛到最优值的一个充分条件.对渐近算法,在弱的假设条件下,证明了算法所得的极小点列有界,且其聚点均为原问题的最优解.并在Mangasarian-Fromovitz约束条件下,证明了有限次迭代之后,所有迭代均为可行的,即迭代所得的极小点为可行点.     

k－Bounded Space On－line装箱中AFB_k算法的界

Ｊ．Ｃｓｉｒｉｋ与Ｄ．Ｓ．Ｊｏｈｎｓｏｎ针对带ｋ－箱限制的在线装箱问题提出了四种装入和关闭法则，并利用这些法则给出了四种相应的算法．其中ＢＢＦｋ，ＮｋＦ和ＡＢＦｋ算法的紧界在文［１－３］中分别进行了很好的研究．但对算法ＡＦＢｋ来讲，其紧界仍是一个公开问题．本文给出了ＡＦＢｋ算法性能比的一个上界，即．同时，本文提出了一个新的关闭法则，对ＡＦＢｋ算法进行了修改，使修改后的算法ＡＦＢｋ的性能比不超过１．７（ｋ３）     

基于自适应动态规划的随机时滞线性二次型最优跟踪控制

针对模型未知且带有时滞的随机线性二次型(SLQ)最优跟踪控制问题,提出了一种自适应动态规划(ADP)算法.首先,利用双因果坐标变换导出原时滞系统的等效系统,构造一个新的由等效系统和命令生成器组成的增广系统,并给出该增广系统的随机代数方程.其次,为了解决随机线性二次最优跟踪控制问题,将随机问题转化为确定性问题.然后提出ADP算法,并给出该算法的收敛性分析.为了实现ADP算法,设计了三种神经网络,分别近似最优性能指标函数,最优控制增益矩阵和系统模型.最后,通过一个数值算例验证算法的有效性.     

基于Gray-Scott系统的图像加密

首先给出了从Gray-Scott系统中提取8位整型伪随机序列的一种有效算法,接着提出了一种使用随机序列的图像像素位置置乱算法,在此基础上结合像素值改变技术设计了一套有效的图像加密方法.数值模拟及安全性分析说明加密方法有效.     

Thomas-Fermi近似问题求解

该文将Ｔｈｏｍａｓ Ｆｅｒｍｉ近似问题分解为一个带奇点的常微分方程边值问题和一个最优化问题，讨论了解的存在唯一性和解的性质，给出了Ｔｈｏｍａｓ Ｆｅｒｍｉ近似问题求解的具体步骤．     

基于近似熵的随机数检验方法的设计与实现

把近似熵用于密码技术中,设计一种实用的随机数检验方法.该方法可以检测随机序列发生器产生序列的随机性,也可以用于检验密码算法的安全性.通过实验,发现该方法能更全面检验序列的随机特性.     

关于拟非扩张映像有限族的一种新的杂交投影算法

在Hilbert空间中针对拟非扩张映像的有限族,我们提出了一种新的杂交投影算法,使用新的分析技巧证明了算法所生成的序列强收敛于拟非扩张映像族的公共不动点,最后我们给出数值实验表明所提出的算法的有效性.     

A note on a dependent risk model with constant interest rate

For a dependent risk model with constant interest rate, in which the claim sizes form a sequence of upper tail asymptotically independent and identically distributed random variables, and their inter-arrival times are another sequence of widely lower orthant dependent and identically distributed random variables, we will give an asymptotically equivalent formula for the finite-time ruin probability. The obtained asymptotics holds uniformly in an arbitrarily finite-time interval.     

über die Definition von effektiven Zufallstests

Summary We continue the discussion on the definition of random sequences from Part I. We will show that the idea of Kolmogoroff to characterize random sequences by their program complexity can be formulated in such a way as to let this definition coÏncide with the others given in Part I. Another equivalent definition of random sequences can be derived from the games of chance. A sequence is random, if and only if no player who calculates his pool by effective methods can raise his fortune indefinitely when playing on this sequence. Finally we will study transformations which preserve the random property of a sequence. We will prove that the original concept of v. Mises can also be modified in such a manner as to coÏncide with all our other definitions. A sequence is random, if and only if it satisfies the strong law of large numbers and if every sequence obtained from it by a constructive measure-preserving transformation is random, too.

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Die Arbeit stellt einen Teil der Habilitationsschrift dar, die der Mathematisch-Naturwissenschaftlichen FakultÄt der UniversitÄt des Saarlandes vom Verfasser vorgelegt wurde.     

Tree Complexity and a Doubly Exponential Gap between Structured and Random Sequences

This paper introduces a new complexity measure for binary sequences, the tree complexity. The tree complexity of a sequence grows asymptotically likeO(2^2h) (hthe height of the tree) for random sequences. Functions inF₂[[x]] can be identified with their coefficient sequence. Under this aspect we will show that the tree complexity isO(1) for all algebraic sequences inF^∞₂. This doubly exponential gap may serve as an indicator of “simply” structured sequences and furthermore it defines certain classes within the vast set of transcendental sequences.     

A block encryption algorithm based on dynamic sequences of multiple chaotic systems

A block encryption algorithm using dynamic sequences generated by multiple chaotic systems is proposed in this paper. In this algorithm, several one-dimension chaotic maps generate pseudo-random sequences, which are independent and approximately uniform. After a series of transformations, the sequences constitute a new pseudo-random sequence uniformly distributing in the value space, which covers the plaintext by executing Exclusive-OR and shifting operations some rounds to form the cipher. This algorithm makes the pseudo-random sequence possess more concealment and noise like characteristic, and overcomes the periodic malpractice caused by the computer precision and single chaotic system. Simulation results show that the algorithm is efficient and useable for the security of communication system.     

Almost Surely Convergent Global Optimization Algorithm Using Noise-Corrupted Observations

A new recursive algorithm for searching the global minimizer of a function is proposed when the function is observed with noise. The algorithm is based on switches between the stochastic approximation and the random search. The combination of SA with RS is not a new idea in such combination, the difficulty consists in creating a good switching rule and in designing an efficient method to reduce the noise effect. The proposed switching rule is easily realizable, the noise reducing method is effective, and the whole recursive optimization algorithm is simply calculated. It is proved that the algorithm a.s. converges to the global minimizer and is asymptotically normal. In comparison with existing methods, the proposed algorithm not only requires much weaker conditions, but also is more efficient as shown by simulation.     

Adaptive Projected Subgradient Method for Asymptotic Minimization of Sequence of Nonnegative Convex Functions

Abstract

This paper presents an algorithm, named adaptive projected subgradient method that can minimize asymptotically a certain sequence of nonnegative convex functions over a closed convex set in a real Hilbert space. The proposed algorithm is a natural extension of the Polyak's subgradient algorithm, for nonsmooth convex optimization problem with a fixed target value, to the case where the convex objective itself keeps changing in the whole process. The main theorem, showing the strong convergence of the algorithm as well as the asymptotic optimality of the sequence generated by the algorithm, can serve as a unified guiding principle of a wide range of set theoretic adaptive filtering schemes for nonstationary random processes. These include not only the existing adaptive filtering techniques; e.g., NLMS, Projected NLMS, Constrained NLMS, APA, and Adaptive parallel outer projection algorithm etc., but also new techniques; e.g., Adaptive parallel min-max projection algorithm, and their embedded constraint versions. Numerical examples show that the proposed techniques are well-suited for robust adaptive signal processing problems.     

The set of stable switching sequences for discrete-time linear switched systems

In this paper we study the characterization of the asymptotical stability for discrete-time switched linear systems. We first translate the system dynamics into a symbolic setting under the framework of symbolic topology. Then by using the ergodic measure theory, a lower bound estimate of Hausdorff dimension of the set of asymptotically stable sequences is obtained. We show that the Hausdorff dimension of the set of asymptotically stable switching sequences is positive if and only if the corresponding switched linear system has at least one asymptotically stable switching sequence. The obtained result reveals an underlying fundamental principle: a switched linear system either possesses uncountable numbers of asymptotically stable switching sequences or has none of them, provided that the switching is arbitrary. We also develop frequency and density indexes to identify those asymptotically stable switching sequences of the system.     

On random variational inclusions with random fuzzy mappings and random relaxed cocoercive mappings

In this paper, we introduce and study the random variational inclusions with random fuzzy and random relaxed cocoercive mappings. We define an iterative algorithm for finding the approximate solutions of this class of variational inclusions and establish the convergence of iterative sequences generated by proposed algorithm. Our results improve and generalize many known corresponding results.     

ASYMPTOTICALLY MEAN ALMOST PERIODIC SOLUTIONS TO RANDOM DIFFERENCE EQUATIONS

In this paper,we first investigate some basic properties of asymptotically mean almost periodic random sequences on Z + and then show some properties of asymptotically mean almost periodic solutions to random difference equations.     

Control issues for the Beverton-Holt equation in ecology by locally monitoring the environment carrying capacity: Non-adaptive and adaptive cases

This paper proposes a control algorithm to govern the solution of the Beverton-Holt equation (BHE) under the potentially presence of additive disturbances. The BHE to be controlled is defined by certain intrinsic growth rate and environment carrying capacity sequences, the last one being susceptible of local modifications around nominal values. In fact, the control action provides the carrying capacity which makes that the solution of the current BHE tracks a reference sequence given by another BHE defined by appropriate intrinsic growth rate and environment carrying capacity sequences. In this context, the fact that the inverse of the BHE is a discrete time-varying linear system is taken into account where the inverse of the carrying capacity sequence plays the role of control sequence. The current and the reference BHEs have to be close enough to each other in order that local modifications of the carrying capacity be able to meet the tracking objective. A feedback control law is designed to achieve such an objective with a zero tracking-error in the ideal case of known intrinsic growth rate sequence and no presence of disturbances. An adaptive control law, with the associated parameter estimation algorithm, is considered when the intrinsic growth rate is fully or partially unknown and disturbances are present. Such a control strategy guarantees a bounded tracking-error with the error converging asymptotically to zero in case that additive disturbances also converge to zero. Some results obtained from a simulation example illustrate the effectiveness of this control strategy.