条件随机变量与条件数据融合

本文讨论基于条件事件代数系统上的条件随机变量的表示问题,并给出了在多个信息源提供的不同类型的条件数据时之统一表示方法,以及利用条件随机变量分布函数性质,对所得数据进行关联等。     

随机频率步进雷达成像分析

随机频率步进信号兼具步进频率雷达和宽带噪声雷达的特点,不仅可以降低系统瞬时带宽和数据采样率,还具有优良的低截获概率(LPI)、电磁兼容性(EMC)和抗射频干扰(ARFI)特性.分析了随机频率步进信号的相关输出及模糊函数的统计特性,如一阶矩、二阶矩和方差等,并与均匀频率步进信号做了抗干扰性能的比较.提出并分析了相关输出结...     

基于证据熵对不确定性度量的决策表约简

知识约简是粗糙集理论的核心内容之一,产生的粗糙决策规则往往具有一定的不确定性.在变精度粗糙集的基础上,本文构造了符合证据理论框架的一组焦元,利用基本概率分配函数计算了证据的总体信息熵,度量了决策表的不确定性;以该度量作为启发信息,给出了决策表的启发式知识约简算法.计算实例表明了本文方法的有效性.     

考虑多轴应力与共振影响的随机振动疲劳寿命预测

目前工程上计算结构随机疲劳寿命时,仍经常采用基于单轴拉-压疲劳寿命(Stress-Number of cycles,S-N)曲线的应力寿命方法.虽然使用简单方便,但这种方法不仅忽视了单轴S-N曲线且不能准确反映结构在多轴应力状态下的疲劳,还忽略了随机振动中共振对结构疲劳寿命的影响,因此该方法在预测结构在随机振动下的疲劳...     

马尔可夫链最优停止的值函数

本文讨论了时间无限的马尔可夫链的最优停止问题。对于无限状态情况,给出了其最优停止变量以及值函数存在的一个充分条件;对于有限状态情况,这个充分条件以及问题的计算等价于解一个线性规划问题。     

考虑黏弹性材料随机性的被动约束层阻尼梁动力学分析

采用传递函数方法研究了阻尼层黏弹性材料随机性对被动约束层阻尼(PCLD)梁动力学特性的影响。由Hamilton原理建立了PCLD梁六阶运动微分方程,通过引入状态向量,建立了系统的状态空间方程,利用传递函数方法得到了梁的固有频率和损耗因子。以黏弹性材料分数导数模型中的参数作为基本的随机变量,并假设其服从正态分布,使用Monte Carlo直接抽样法考察了材料模型参数的随机性对结构固有频率和模态损耗因子的影响。计算结果表明黏弹性材料参数的随机性对梁动力学特性的变异系数影响较大,模态损耗因子的变异系数最大值是材料参数变异系数的4.5倍。     

有限马氏链最优停止的线性规划解

离散时间的有限状态马尔可夫链最优停止的值函数存在的一个充分条件是所对应的线性规划有解,且其最优解等于值函数,本文证明这个条件还是必要的。     

Adaptive random search for continuous simulation optimization

We present, analyze, and compare three random search methods for solving stochastic optimization problems with uncountable feasible regions. Our adaptive search with resampling (ASR) approach is a framework for designing provably convergent algorithms that are adaptive and may consequently involve local search. The deterministic and stochastic shrinking ball (DSB and SSB) approaches are also convergent, but they are based on pure random search with the only difference being the estimator of the optimal solution [the DSB method was originally proposed and analyzed by Baumert and Smith]. The three methods use different techniques to reduce the effects of noise in the estimated objective function values. Our ASR method achieves this goal through resampling of already sampled points, whereas the DSB and SSB approaches address it by averaging observations in balls that shrink with time. We present conditions under which the three methods are convergent, both in probability and almost surely, and provide a limited computational study aimed at comparing the methods. Although further investigation is needed, our numerical results suggest that the ASR approach is promising, especially for difficult problems where the probability of identifying good solutions using pure random search is small. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Averaging frameworks for simulation optimization with applications to simulated annealing

We present two frameworks for designing random search methods for discrete simulation optimization. One of our frameworks is very broad (in that it includes many random search methods), whereas the other one considers a special class of random search methods called point‐based methods, that move iteratively between points within the feasible region. Our frameworks involve averaging, in that all decisions that require estimates of the objective function values at various feasible solutions are based on the averages of all observations collected at these solutions so far. Also, the methods are adaptive in that they can use information gathered in previous iterations to decide how simulation effort is expended in the current iteration. We show that the methods within our frameworks are almost surely globally convergent under mild conditions. Thus, the generality of our frameworks and associated convergence guarantees makes the frameworks useful to algorithm developers wishing to design efficient and rigorous procedures for simulation optimization. We also present two variants of the simulated annealing (SA) algorithm and provide their convergence analysis as example application of our point‐based framework. Finally, we provide numerical results that demonstrate the empirical effectiveness of averaging and adaptivity in the context of SA. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

一种基于MMMA的双模式变步长盲均衡算法

针对传统修正恒模算法(MCMA)收敛速度慢、剩余码间干扰大的缺点,在双模式修正恒模算法(MCMA+DD-LMS)的基础上引入局部信息量和变步长的思想,提出了一种基于修正多模算法(MMMA)的双模式变步长修正多模算法(VS-MMMA+DD-LMS)。经过理论分析和实验仿真,证明改进后的算法较传统算法具有更好的均衡效果。