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Dynamic information aggregation decision-making methods based on variable precision rough set and grey clustering

机译:基于变精度粗糙集和灰色聚类的动态信息聚合决策方法

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Purpose - The purpose of this paper is to construct a dynamic information aggregation decision-making model based on variable precision rough set. Design/methodology/approach - To deal with the dynamic decision-making problems, the grey relational analysis method, grey fixed weight clustering based on the centre triangle whitening weight function and maximum entropy principle is used to establish the dynamic information aggregation decision-making model based on variable precision rough set. The method, to begin with, the grey relational analysis method is used to determine the attributes weights of each stage; taking the proximity of the attribute measurement value and positive and negative desired effect value and the uncertainty of time weight into account, a multi-objective optimisation model based on maximum entropy principle is established to solve the model with Lagrange multiplier method, so that time weights expression are acquired; what is more, the decision-making attribute is obtained by grey fixed weight clustering based on the centre triangle whitening weight function, so that multi-decision-making table with dynamic characteristics is established, and then probabilistic decision rules from multi-criteria decision table are derived by applying variable precision rough set. Finally, a decision-making model validates the feasibility and effectiveness of the model. Findings - The results show that it the proposed model can well aggregate the multi-stage dynamic decision-making information, realise the extraction of decision-making rules. Research limitations/implications - The method exposed in the paper can be used to deal with the decision-making problems with the multi-stage dynamic characteristics, and decision-making attributes contain noise data and the attribute values are interval grey numbers. Originality/value - The paper succeeds in realising both the aggregation of dynamic decision-making information and the extraction of decision-making rules.
机译:目的-本文的目的是构建基于可变精度粗糙集的动态信息聚合决策模型。设计/方法/方法-为解决动态决策问题,使用灰色关联分析方法,基于中心三角白化权函数的灰色固定权聚类和最大熵原理建立动态信息聚合决策模型基于可变精度的粗糙集。首先,使用灰色关联分析法确定每个阶段的属性权重。考虑到属性测量值与正负期望效果值的接近度以及时间权重的不确定性,建立了基于最大熵原理的多目标优化模型,以拉格朗日乘数法求解该模型,从而得到了时间权重。获得表达;此外,基于中心三角白化权函数,通过灰色固定权聚类得到决策属性,建立具有动态特征的多决策表,然后从多准则决策表中得出概率决策规则。通过应用可变精度的粗糙集得出。最后,决策模型验证了该模型的可行性和有效性。结果-结果表明,所提出的模型能够很好地汇总多阶段动态决策信息,实现决策规则的提取。研究的局限性/意义-本文公开的方法可用于处理具有多阶段动态特征的决策问题,决策属性包含噪声数据,而属性值为区间灰度数字。原创性/价值-本文成功地实现了动态决策信息的汇总和决策规则的提取。

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