Iterative analysis of Markov regenerative models

Conventional algorithms for the steady-state analysis of Markov regenerative models suffer from high computational costs which are caused by densely populated matrices. In this paper, a new algorithm is suggested which avoids computing these matrices explicitly. Instead, a two-stage iteration scheme is used. An extended version of uniformization is applied as a subalgorithm to compute the required transient quantities “on-the fly”. The algorithm is formulated in terms of stochastic Petri nets. A detailed example illustrates the proposed concepts.     

Geoadditive expectile regression

Quantile regression has emerged as one of the standard tools for regression analysis that enables a proper assessment of the complete conditional distribution of responses even in the presence of heteroscedastic errors. Quantile regression estimates are obtained by minimising an asymmetrically weighted sum of absolute deviations from the regression line, a decision theoretic formulation of the estimation problem that avoids a full specification of the error term distribution. Recent advances in mean regression have concentrated on making the regression structure more flexible by including nonlinear effects of continuous covariates, random effects or spatial effects. These extensions often rely on penalised least squares or penalised likelihood estimation with quadratic penalties and may therefore be difficult to combine with the linear programming approaches often considered in quantile regression. As a consequence, geoadditive expectile regression based on minimising an asymmetrically weighted sum of squared residuals is introduced. Different estimation procedures are presented including least asymmetrically weighted squares, boosting and restricted expectile regression. The properties of these procedures are investigated in a simulation study and an analysis on rental fees in Munich is provided where the geoadditive specification allows for an analysis of nonlinear effects of the size of flats or the year of construction and the spatial distribution of rents simultaneously.     

水工混凝土结构裂缝成因挖掘的粗集方法研究

裂缝是水工混凝土结构常见病害之一,针对水工混凝土结构运行期的裂缝问题,应用粗集理论,结合实测资料,对裂缝产生的主要原因进行了系统地研究,提出了裂缝成因挖掘的粗集方法.     

神经网络在煤矿安全决策支持系统中的研究及应用

主要研究人工神经网络在煤矿安全决策支持系统中的应用。将BP网络用于瓦斯突出,解决了瓦斯突出判别困难、难以建立数学模型的难题,为煤矿瓦斯突出研究找到了一种新的建模和预测方法。     

加强企业统计工作的思考

企业统计是生产经营成果化、数量化的数字表现，是经济社会各项主要统计指标的源头数据之一。因此强化企业统计，加快统计信息网络建设，提高综合统计人员的素质，对企业的经营、对政府的宏观调控有着重要意义。     

带有区间约束的产品设计参数模糊推理法及其在继电器智能设计中的应用

在产品设计规则和计算式中,常存在一些只能在某一范围内取值的待定系数(设为a,且a∈[aL,aH]).a的取值通常受到多种因素的影响和约束,而这种约束往往带有模糊性.为此,首先根据设计规则中各种影响因素对a的模糊约束关系,分别构造两者的备择集以及备择集元素在其论域上的模糊集合,然后基于模糊综合决策方法来确定a.本方法剔除了以往在解决这一问题时,由人来决定a取值的主观性,适用于产品的智能化设计.     

对隐马尔科夫模型中动态编程的探讨

主要对隐马尔科夫模型中的三个问题（估计问题、译码问题、学习问题）中的译码问题作了研究．并对其应用进行了探讨。     

面向业务分析与预测的企业决策支持系统设计

为了提高企业决策自动化管理水平，运用先进的数据仓库和数据挖掘技术，采用请求响应方式以及规范化的接口结构，提出一个企业信息分析及决策系统的设计和解决方案。根据完善的智能化分析手段，有效的保证决策者的经验与决策支持系统的分析和预测能力相结合，并对企业管理者提供科学准确的分析预测和决策支持。系统可实时地为用户提供有效详细的分析数据，从而大大加快决策进程并有效提高准确性。     

Total Travel Cost in Stochastic Assignment Models

Deterministic assignment models are sometimes used to approximate properties of more complex stochastic models. One property that is of particular interest from a system optimization viewpoint is total travel cost. This paper looks at the approximation of mean total travel cost. It is shown that deterministic models will underestimate this quantity in many common situations. Furthermore, discrepancies between total travel cost under the different modelling frameworks can lead to situations in which network modifications which are detrimental according to a stochastic model appear beneficial when using the natural deterministic approximation. We conclude that estimation of mean travel cost in stochastic assignment is often best done using simulation. Some suggestions are made regarding the implementation of traffic assignment simulation.     

String-matching with OBDDs

Ordered binary decision diagrams (OBDDs) are a very popular graph representation for Boolean functions. They can be viewed as finite automata recognizing sets of strings of a fixed length, where the letters of the input strings are read at most once in a predefined ordering. The string matching problem with string w as pattern, consists of determining, given an input string, whether or not it contains w as substring. We show that for a fraction of orderings tending to 1 when n increases arbitrarily, the minimal size of an OBDD solving the string matching problem for strings of length n has a growth which is an exponential in n.