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1)  set-valued statistics
集值统计原理
1.
The uncertain type of analytic hierarchy process(AHP) method,fuzzy theory and set-valued statistics theory are applied to the comprehensive assessment of long span cable-stayed bridges,and then an index system and an assessment model are proposed for comprehensive assessment of cable-stayed bridges.
将不确定型层次分析法、模糊理论、集值统计原理等方法应用于大跨度斜拉桥的综合评估中,建立了斜拉桥综合评估的指标体系和评估模型,并通过哈尔滨松花江斜拉桥综合评估的实例,证明了该评估体系的可行性与实用性。
2)  set valued statistics
集值统计
1.
In this paper, Set Valued Statistics Based Fuzzy Neural Expert Evaluation System is presented.
建立基于集值统计的模糊神经网络专家评审系统 ,并应用于科研项目评审工作 ,实际应用表明该系统是可行
3)  set-valued statistics
集值统计
1.
Project appraisal model based on set-valued statistics and its application;
基于集值统计的项目评审模型及其应用
2.
Evaluation method of cabin fire-fatalness based on set-valued statistics;
基于集值统计的船舶舱室火灾危险性评估
3.
In the paper, a multi-objective comprehensivc decision model of determining the optimal mining technology scheme for coalface is set up by using set-valued statistics principle and multi-objective decision method, and an applied example is given.
应用集值统计原理及多目标决策方法建立了一种确定工作面最优采煤工艺方案的多目标综合决策模型,并给出了应用实例。
4)  set-value statistics
集值统计
1.
The evaluation and forecasting of technology innovation risk based on set-value statistics;
基于集值统计的技术创新风险度量及其预警
2.
Based on the theory of set-value statistics,a new judgment matrix consisting of some defining-numbers with certain reliabilities is constructed equivalent to judgment matrixes with interval-number,and the model of computing and revising the reliable degree of the aggregated values of group information is established.
借助于集值统计原理,构造了与区间数判断矩阵群信息等价的具有一定可靠度的确定数判断矩阵,建立了计算这类群信息集结值可靠度的模型,运用和积法对所得新判断矩阵进行处理,得到了一种简便的方案排序方法。
5)  set statistics
集值统计
1.
A method of weapon evaluation by means of level analysis of set statistics is established based on the falling projection theory of fuzzy math.
应用模糊数学中落影理论所确定的集值统计程度分析法,建立了一种武器评价方法。
6)  the theory of fuzzy centralization statistic
模糊集值统计理论
补充资料:统计量值和接受域
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性质:统计量是一个随机变量,由一次抽样检验所得到的统计量值以一定的概率出现,只是概率的大小不同而已。在对假设进行抽样检验时,即使原假设H0不正确,在某一次抽样检验时,统计量值也可能以一个小的概率β出现在接受原假设的区域,而错误地接受了原假设H0;反之,即使原假设正确,在某一次抽样检验时,统计量值也可能以一个小的概率α出现在拒绝原假设H0的区域,而错误地拒绝了正确的原假设H0,1-α则为原假设H0为真时而作出接受原假设H0的结论的概率。这个以1-α概率正确能接受原假设H0的区域,称为接受域。

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