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Adding Compensatory Terms in the Fuzzy Integral for Synthetic Evaluation of Multi-attribute Alternatives

机译:在模糊积分中添加补偿项以对多属性替代方案进行综合评估

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The fuzzy integral (FI) is an interesting and often advantageous method of obtaining synthetic evaluations of objects that possess several qualitative attributes. A synthetic evaluation of each prospect choice is represented by a single real number, which is convenient for establishing a ranking of preferences for a group of objects. The need of synthetic evaluations is particularly important in expert systems where an ordering of the feasible alternatives is expected. However, the traditional formulation of the FI lacks sensitivity to some changes that may occur in the scores that are assigned to particular characteristics of an object under appraisal. In this paper, a method to take into account the variations in the scores that are not captured by the max-min operations performed by the traditional FI is presented. For clarity, an example of application of the proposed process follows the theoretical considerations. Using the numerical data from the example, the results yielded by the approaches regarding the original FI and its modified version incorporating the compensatory terms are compared and briefly discussed.
机译:模糊积分(FI)是获得具有多个定性属性的对象的综合评估的一种有趣且通常是有利的方法。每个潜在客户选择的综合评估由单个实数表示,这对于建立一组对象的偏好排名很方便。在需要对可行替代方案进行排序的专家系统中,综合评估的需求尤其重要。但是,FI的传统形式对分配给待评估对象的特定特征的分数可能发生的某些变化缺乏敏感性。在本文中,提出了一种方法,该方法考虑了传统FI执行的最大-最小运算未捕获的分数变化。为了清楚起见,所提出的方法的应用实例遵循理论上的考虑。使用示例中的数值数据,比较并简要讨论了有关原始FI及其包含补偿项的修改版本的方法所产生的结果。

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