对城市互通立交线形设计安全性探索和分析

城市互通立交线形设计过程中存在很多的安全影响因素,因此正确认识到这些安全隐患,对于提高道路的实用性和稳定性有着重要意义。     

基于Bayes判别法的脑电图数据分析的研究

目的:本文通过对客观记录的受试者脑电图数据进行Bayes判别分析,判断其能否应用于脑电数据特征提取和分类决策。为脑电图研究的其它分析做基础分析。方法:根据α波的强弱不同将21导电极分为四类,分别对63例正常状态下受试者21导联电极的脑电图数据进行Bayes判别分析,并利用误判率回代估计法检验判别准确率。数据处理和统计分析采用独立设计的脑电图分析工具箱和Bayes判别分析程序。结果:表明对63例正常状态下受试者的脑电图数据进行Bayes判别分析,预测各电极分类准确率75.4%。结论:Bayes判别法预测准确率较高,脑电特征(主要为α波)提取较为准确,能较好的应用于脑电数据特征提取和分类决策中,从而辅助脑电图的检查和定量分析,为脑电图的检验提供有效的分析手段。     

A股市场新股的因素定价模型实证研究

现代股权定价理论对A股初始定价时存在缺陷,采用主成份回归法,确定影响A股初始定价的主因子,在此基础上建立初始定价模型。根据模型,影响A股初始定价的因素从高到低分别为:成本因子、投资偏好因子、市况因子、业绩成长性因子,实证结果与传统股票定价理论并不吻合。     

物流线性规划问题的MATLAB求解

对线性规划问题的图解法、单纯形解法,以及MATLAB的函数linprog求解方法进行了对比分析,并结合实例,采用linprog函数对产销平衡和产销不平衡的两类物流运输问题进行了求解。     

基于多元线性回归的上海市生产性服务业和汽车产业发展的关联分析

生产性服务业是上海市“十二五”重点发展产业,“十二五”期间是上海市汽车产业的关键时期,发展基于汽车行业的生产性服务业将有效促进上海市两大关键产业的发展.从上海市生产性服务业和汽车产业发展的实践出发,基于生产性服务业和汽车行业的关联性概念模型,应用多元线性回归技术,通过对上海市生产性服务业、汽车产业及其外部环境因素的多元线性回归,论文研究了上海市生产性服务业、汽车产业和外部环境的数量关联及其含义.     

浅析线性规划方法在半导体制造计划中的应用

半导体制造业中产品种类繁多,工序复杂,对设备的利用率要求较高,因而相对于其它制造也来说,生产计划的优化也较为复杂。本文讨论了目前半导体行业中常用的启发算法和基于线性规划的优化方法。     

A DC programming heuristic applied to the logistics network design problem

This paper proposes a new heuristic method for the logistics network design and planning problem based on linear relaxation and DC (difference of convex functions) programming. We consider a multi-period, multi-echelon, multi-commodity and multi-product problem defined as a large scale mixed integer linear programming (MILP) model. The method is experimented on data sets of various size. The numerical results validate the efficiency of the heuristic for instances with up to several dozens facilities, 18 products and 270 retailers.     

Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times

In this paper, we consider a flowshop scheduling problem with sequence-dependent setup times and a bicriteria objective to minimize the work-in-process inventory for the producer and to maximize the customers' service level. The use of a bicriteria objective is motivated by the fact that successful companies in today's environment not only try to minimize their own cost but also try to fulfill their customers' need. Two main approaches, permutation and non-permutation schedules, are considered in finding the optimal schedule for a flowshop. In permutation schedules the sequence of jobs remains the same on all machines whereas in non-permutation schedule, jobs can have different sequence on different machines. A linear mathematical model for solving the non-permutation flowshop is developed to comply with all of the operational constraints commonly encountered in the industry, including dynamic machine availabilities, dynamic job releases, and the possibility of jobs skipping one or more machines, should their operational requirements deem that it was necessary. As the model is shown to be NP-hard, a metasearch heuristic, employing a newly developed concept known as the Tabu search with embedded progressive perturbation (TSEPP) is developed to solve, in particular, industry-size problems efficiently. The effectiveness and efficiency of the search algorithm are assessed by comparing the search algorithmic solutions with that of the optimal solutions obtained from CPLEX in solvable small problem instances.     

高科技风投企业家项目考察阶段的非理性决策实证研究——基于人口统计特征分析

基于人口统计特征,采用列联表和多元线性回归的方法分析人口统计特征中有关的年龄、学历、留学经历与家庭结构等要素与高科技风投企业家非理性行为在项目考察阶段决策的相关性结果显示:国外留学经验、创业成功经验与企业家项目考察阶段的非理性决策存在着相关性。     

Constrained Kalman Filtering: Additional Results

This paper deals with linear state space modelling subject to general linear constraints on the state vector. The discussion concentrates on four topics: the constrained Kalman filtering versus the recursive restricted least squares estimator; a new proof of the constrained Kalman filtering under a conditional expectation framework; linear constraints under a reduced state space modelling; and state vector prediction under linear constraints. The techniques proposed are illustrated in two real problems. The first problem is related to investment analysis under a dynamic factor model, whereas the second is about making constrained predictions within a GDP benchmarking estimation.