基于遗传算法的变风量空调模糊控制系统的研究

针对变风量空调模糊控制系统控制规则的获取依赖人类经验的问题,提出将遗传算法与模糊控制相结合。在推导适应度值函数的基础上,利用遗传算法获取控制规则,改善控制效果。一定程度上解决了人为调试获取规则困难的问题。仿真结果表明,在变风量空调系统中将遗传算法与模糊控制相结合是可行的。     

基于DV-Hop误差修正的煤矿井下人员定位算法

为了提高无线传感器网络的节点定位精度,提出了基于DV-Hop误差修正的质心定位算法.针对煤矿井下巷道的线型结构,首先提出信标节点以均匀间隔交叉分布的模型,再根据各个信标节点平均每跳的距离误差以及平均每跳的单位距离误差,对离未知节点最近的3个信标节点之间的距离进行2次修正,最后利用3圆交点的质心定位算法进行定位.仿真结果表明,该算法比目前的定位精度提高1m左右,比较适合煤矿井下这种特定的线型结构.     

危险货物零担运输多目标配装问题及遗传算法求解

零担货物具有运量零星、批数较多的特点,而危险货物又有混装的限制,所以危险货物零担运输配装问题一直是铁路部门复杂而急需解决的实际问题.在给出其数学模型的基础上,先对待装货物进行预处理,然后运用遗传算法对问题进行了求解,最后通过实例证明该方法的可行性和有效性.     

Dynamic Niching Genetic Algorithm with Data Attraction for Automatic Clustering

A genetic clustering algorithm was developed based on dynamic niching with data attraction. The algorithm uses the concept of Coulomb attraction to model the attraction between data points. Then, the niches with data attraction are dynamically identified in each generation to automatically evolve the optimal number of clusters as well as the cluster centers of the data set without using cluster validity functions or a variance-covariance matrix. Therefore, this clustering scheme does not need to pre-specify the number of clusters as in existing methods. Several data sets with widely varying characteristics are used to demonstrate the superiority of this algorithm. Experimental results show that the performance of this clustering algorithm is high, effective, and flexible.     

基于层次模型数据库的算法设计

层次模型是DBMS所支持的三大数据模型之一，本文述了如何利用关系模型表达层次模型的一种方法，具体给出了插入和修改的算法，此算法简便，可行，有效，不需对数据库进行规范化处理，此算法已在Delphi 3.0中实现，效果显著，具有友好，快捷的操作界面。     

强单调逆变分不等式的自适应投影算法

利用投影技巧给出求解强单调逆变分不等式的自适应投影算法,并在适当假设条件下,证明了算法的收敛性.     

地方高校研究机构绩效评估指标权重的AHP算法

根据地方高校研究机构的科研特点，探讨了运用AHP理论确定高校研究机构绩效评估指标权重的方法。     

关于广义非线性隐似变分包含问题

文章在Hilbert空间中引入了一类新的涉及（A，η）单调映射的非线性集值隐似变分包含问题，基于与（A，η）单调性相关的广义预解算子技巧，用一种迭代算法研究了解的近似可解性，所得结果改进了许多近期结果．     

图书馆知识服务团队的协同构建

依据影响团队建设的系统科学协同效应理论,从组织、人员、机制等入手,对图书馆构建知识服务团队的结构层次、人员配置、服务环境、作用机理进行了分析,以实现图书馆知识服务团队建设的标准化及知识服务团队服务能力的全面提升。     

Strategic Robust Mixed Model Assembly Line Balancing Based on Scenario Planning

Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a deterministic or stochastic/fuzzy model yields quite poor performance in reality. In real environments, assembly line balancing robustness is a more appropriate decision selection guide. A robust model based on the α worst case scenario is developed to compensate for the drawbacks of traditional robust criteria. A robust genetic algorithm is used to solve the problem. Comprehensive computational experiments to study the effect of the solution procedure show that the model generates more flexible robust solutions. Careful tuning the value of α allows the decision maker to balance robustness and conservativeness of as- sembly line task element assignments.