Convergence with a fixed regulator in lattice ordered groups and applications to MV-algebras

A convergence with a fixed regulator v in lattice ordered groups is applied to MV-algebras. For each Archimedean MV-algebra A there exists a v-Cauchy completion A ^* and it is uniquely determined up to isomorphisms over A. The relation between the Dedekind completion A ^∧ of A and A ^* is established. There is solved a question of the existence of the greatest v-Cauchy complete ideal of A.     

On the Power of Las Vegas for One-Way Communication Complexity, OBDDs, and Finite Automata

The study of the computational power of randomized computations is one of the central tasks of complexity theory. The main goal of this paper is the comparison of the power of Las Vegas computation and deterministic respectively nondeterministic computation. We investigate the power of Las Vegas computation for the complexity measures of one-way communication, ordered binary decision diagrams, and finite automata.(i) For the one-way communication complexity of two-party protocols we show that Las Vegas communication can save at most one half of the deterministic one-way communication complexity. We also present a language for which this gap is tight.(ii) The result (i) is applied to show an at most polynomial gap between determinism and Las Vegas for ordered binary decision diagrams.(iii) For the size (i.e., the number of states) of finite automata we show that the size of Las Vegas finite automata recognizing a language L is at least the square root of the size of the minimal deterministic finite automaton recognizing L. Using a specific language we verify the optimality of this lower bound.     

A Comparison of Free BDDs and Transformed BDDs

Ordered binary decision diagrams (OBDDs) introduced by Bryant (IEEE Trans. on Computers, Vol. 35, pp. 677–691, 1986) have found a lot of applications in verification and CAD. Their use is limited if the OBDD size of the considered functions is too large. Therefore, a variety of generalized BDD models has been presented, among them FBDDs (free BDDs) and TBDDs (transformed BDDs). Here the quite tight relations between these models are revealed and their advantages and disadvantages are discussed.     

Local Encoding Transformations for Optimizing OBDD-Representations of Finite State Machines

Ordered binary decision diagrams are the state-of-the-art representation of switching functions. In order to keep the sizes of OBDDs tractable, heuristics and dynamic reordering algorithms are applied to optimize the underlying variable order. When finite state machines are represented by OBDDs the state encoding can be used as an additional optimization parameter. In this paper, we analyze local encoding transformations which can be applied dynamically. First, we investigate the potential of re-encoding techniques. We then propose the use of an XOR-transformation and show why this transformation is most suitable among the set of all encoding transformations. The presented theoretical framework establishes a new optimization technique for OBDDs.     

模糊诱导有序加权平均算子

给出了诱导有序加权平均算子的矩阵表示,在此基础上提出了模糊诱导有序加权平均算子,讨论了模糊诱导有序加权平均算子的相关性质,并将模糊诱导有序加权平均算子用于一个投资决策实例分析中。     

缫丝排序算法

文中提出一种改进的排序算法,弥补了快速排序在大规模下堆栈低效及合并排序在小规模下优势不明显的问题.算法扩展了合并排序思想,从一种特殊的蚕茧缫丝工艺得到启发,使用2～6个滚轴分离待排序列中的有序片段,在滚轴始末端扩展新数据,从而达到在合并操作前增加有序子序列长度的目的.理论推导表明,缫丝排序中的基本操作数量较合并排序减少4.75N,相当于将待排序列缩小至原有规模的1/4;效率测试实验表明,缫丝排序在各种规模下均能获得相比最快经典排序算法10％～15％的稳定优势,相比前人的改进排序算法具备相当的互补性,并能有效降低排序库函数自适应选择算法的实现复杂度.     

基于包含度的不完备序信息系统属性约简的一种算法

在不完备序信息系统中,通过引入包含度的概念,对属性重要度进行了定义;在此基础上,提出了一种基于包含度的属性约简算法;最后,通过实例分析表明,该算法能得到不完备序信息系统的一个约简.     

计及机组可靠性的燃煤机组有序调停经济性调度研究

某省自实施有序调停以来,取得良好节能减排效果。本文就有序调停问题建立目标规划模型,并根据实际情况及研究调查,确定权系数,良好的对有序调停问题进行约束,并在此基础上,解决在传统线性规划问题中,找不到最优解的情况,本文所采用的模型在尽量满足约束条件的基础上,寻求满意解。在实例分析中,通过8台机组的小型网络决策过程,验证了这一方法的有效性,并得出节约的经济效益。     

虚拟弧齿锥齿轮齿面可控性椭球抛物面误差的叠加新方法

为了研究弧齿锥齿轮测量精度稳定性及不同程序测量误差的问题,提出了一种对虚拟弧齿锥齿轮进行可控误差叠加的方法.分析虚拟弧齿锥齿轮建立时产生的误差,在虚拟工件上叠加可控的椭球抛物面误差,调用OpenGL图形库中的函数将叠加误差后的效果进行直观对比.研究结果表明:对虚拟弧齿锥齿轮进行误差可控的叠加,得到了齿面误差可控的虚拟弧齿锥齿轮.     

基于有序爬山法的前向启发式搜索规划

根据快速前向启发式搜索规划器FF中放宽规划图和有利动作之间的关系,定义了状态适用度函数的概念,可对后继扩展状态的启发式估值进行快速近似的比较。在此基础上,结合增强型爬山法搜索后继状态的贪婪选择机制,提出了一种改进的局部搜索算法——有序爬山法,即根据状态适应度函数对所有待扩展的后继状态进行排序,并加入到扩展优先队列。在启发式估值阶段,适应度高的状态将被优先计算评估,有利于更快地发现较优状态,从而减少调用启发式估值程序的次数。通过在国际规划大赛基准测试领域的实验结果表明,该方法减少了搜索节点的数目和搜索时间,有效地提高了启发式搜索效率,而计算状态适应度及对状态进行排序的时间消耗几乎可以忽略,因此整体规划性能比FF有显著的提升。