A new generation of process capability indices based on fuzzy measurements

Process capability indices (PCIs) provide numerical measures on whether a process conforms to the defined manufacturing capability prerequisite. These have been successfully applied by companies to compete with and to lead high-profit markets by evaluating the quality and productivity performance. The PCI C_p compares the output of a process to the specification limits (SLs) by forming the ratio of the width between the process SLs with the width of the natural tolerance limits which is measured by six process standard deviation units. As another common PCI, C_pm incorporates two variation components which are variation to the process mean and deviation of the process mean from the target. A meaningful generalized version of above PCIs is introduced in this paper which is able to handle in a fuzzy environment. These generalized PCIs are able to measure the capability of a fuzzy-valued process in producing products on the basis of a fuzzy quality. Fast computing formulas for the generalized PCIs are computed for normal and symmetric triangular fuzzy observations, where the fuzzy quality is defined by linear and exponential fuzzy SLs. A practical example is presented to show the performance of proposed indices.     

车辆路径问题的模型及算法研究综述

本文在文献[1,2,3,4]的基础上,首先,介绍了车辆路径问题的分类和限制条件;然后,全面综述了国内外关于车辆路径问题的模型及算法研究现状,重点探讨了车辆路径问题的模型构造、求解算法及其适用范围;最后,展望了其研究的前景。     

均值不等式的八种证法

由于不等式本身在数学中的重要地位以及不等式的证明的困难性,使不等式的证明方法成为数学领域内的热门问题.本文拟将介绍均值不等式的算术归纳法、局部调整法、排序原理、不等式法、几何方法、变量替换法、归纳原理、逐次调整法等八种证明方法,归纳总结出不等式证明技巧,进而提高学习者不等式探究能力和证明方法.     

The predictive distributions of thinning‐based count processes

This paper shows that the term structure of conditional (i.e. predictive) distributions allows for closed form expression in a large family of (possibly higher order or infinite order) thinning‐based count processes such as INAR(p), INARCH(p), NBAR(p), and INGARCH(1,1). Such predictive distributions are currently often deemed intractable by the literature and existing approximation methods are usually time consuming and induce approximation errors. In this paper, we propose a Taylor's expansion algorithm for these predictive distributions, which is both exact and fast. Through extensive simulation exercises, we demonstrate its advantages with respect to existing methods in terms of the computational gain and/or precision.     

关于平均指标灵敏性问题的探讨

从相对影响角度看 ,在极端大值的情况下 ,算术平均数最灵敏 ,几何平均数次之 ,调和平均数最不灵敏 ;在极端小值的情况下 ,算术平均数最不灵敏 ,几何平均数次之 ,调和平均数最灵敏 .从绝对影响角度看 ,在极端大值的情况下 ,算术平均数最灵敏 ;在极端小值的情况下 ,算术平均数最不灵敏 ;几何平均数与调和平均数的灵敏程度关系有待进一步研究     

中小学数学教学衔接问题的探讨

本文从九年义务教育中小学教学教材的知识结构出发，阐述了中小学数学概念教学、数运算与式运算教学、应用题教学、思维方法的培养与几何知识教学等方面的衔接问题，探讨了衔接的途径与作法．     

关联规则在教学管理决策支持中的应用

运用数据挖掘中的关联规则分析了教学管理中教师信息之间的隐藏关系．并对数据进行了标准化、离散化处理，采用优化的Apriori算法进行数据挖掘．通过事例分析了教师的教学工作量和发表论文之间的隐含关系，可为教学管理提供决策支持．     

基于快速BP算法的ANN模型在财务失败预测上的应用

对企业财务状况的正确判断关系着企业的生存和发展,因此建立合理的财务失败预测模型是财务研究的热点。在企业财务失败预测模型中引入基于快速BP算法的人工神经网络,克服了标准BP算法的不足,比Fisher判别分析模型有着更强的预测能力。     

快速神经网络无损压缩方法研究

传统的人工神经网络数据编码算法需要离线训练且编码速度慢,因此通常多用于专用有损编码领域如声音、图像编码等,在无损数据编码领域应用较少。针对这种现状,该文详细地研究了最大熵统计模型和神经网络算法各自的特点,提出了一种基于最大熵原理的神经网络概率预测模型并结合自适应算术编码来进行数据压缩,具有精简的网络结构的自适应在线学习算法。试验表明,该算法在压缩率上可以优于目前流行的压缩算法Limpel-Zip(zip,gzip),并且在运行时间和所需空间性能上同PPM和Burrows Wheeler算法相比也是颇具竞争力的。该算法实现为多输入和单输出的两层神经网络,用已编码比特的学习结果作为待编码比特的工作参数,符合数据上下文相关约束的特点,提高了预测精度,并节约了编码时间。     

密钥共享方案中欺骗者的认定

为了增强Ｓｈａｍｉｒ密钥共享的（ｋ，ｎ）门限方案的安全性，文中基于单向哈希函数介绍了一种简单的欺骗者认定方法。该欺骗者认定方法引入算术编码原理之后不仅能检测出密钥重构过程中的欺骗行为，而且能确定谁是欺骗者。