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A dynamic stochastic decision-making method based on discrete time sequences

机译:基于离散时间序列的动态随机决策方法

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摘要

This paper studies a dynamic stochastic decision-making method based on discrete time sequences. With the aim of rectifying the deficiency of calculation methods that use only the "sequence" information in the determination of discrete time sequence weight, we first propose a discrete time sequence weight calculation method that can deal with unequal time intervals by introducing an exponential decay model, and gather the original dynamic stochastic decision information according to the timing weight and attribute weight. Second, considering the disadvantages of decision criteria such as traditional stochastic dominance, stochastic multi-criteria acceptability analysis, and connection number of set pair analysis, in the absence of original information and limited conditions, we propose a decision method based on possibility, convert this information into interval numbers based on the theory of stochastic probability distribution, and then rank the order of the scheme using the possibility model of interval numbers. Finally, we test the effectiveness and reasonableness of the method by empirical calculation and show that the proposed method is both practical and effective. (C) 2016 Elsevier B.V. All rights reserved.
机译:本文研究了一种基于离散时间序列的动态随机决策方法。为了纠正在确定离散时间序列权重时仅使用“序列”信息的计算方法的不足,我们首先提出一种离散时间序列权重计算方法,该方法可以通过引入指数衰减模型来处理不等时间间隔,并根据时序权重和属性权重收集原始的动态随机决策信息。其次,考虑到决策准则的缺点,如传统的随机优势,随机多准则可接受性分析和集合对分析的连接数,在没有原始信息和有限条件的情况下,我们提出了一种基于可能性的决策方法,将其转换为根据随机概率分布理论将信息划分为区间数,然后使用区间数的可能性模型对方案进行排序。最后,通过经验计算验证了该方法的有效性和合理性,证明了该方法的实用性和有效性。 (C)2016 Elsevier B.V.保留所有权利。

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