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FIF: A fuzzy information fusion algorithm based on multi-criteria decision making

机译：FIF：基于多准则决策的模糊信息融合算法

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摘要

The main goal of information fusion is to combine heterogeneous information to obtain a single composite of potential comparable alternative solutions that can be classified and ranked. The crux of information fusion, which is a type of data fusion, is threefold: (ⅰ) data must be comparable and numerical, using some normalization process; (ⅱ) imprecision in data must be taken into consideration; (ⅲ) an appropriate aggregation function to combine values into a single score must be selected. Recently, computational intelligence concepts and techniques to perform data/information fusion are emerging as suitable tools. Although with a different perspective, another field where much work has also been done for combining heterogeneous information is multi-criteria decision-making. In general, multi-criteria problems are modelled by choosing a set of relevant criteria -usually dealing with heterogeneous data -that have to be aggregated (i.e. fused) to obtain a single rating for each candidate alternative. In this paper we propose an algorithm for data/information fusion, which includes concepts from multi-criteria decision-making and computational intelligence, specifically, fuzzy multi-criteria decision-making and mixture aggregation operators with weighting functions. The application field of interest for this work is safe spacecraft landing with hazard avoidance; hence two existing hazard maps will be used to illustrate the versatility of the algorithm.
机译：信息融合的主要目标是组合异构信息，以获得可以分类和排名的潜在可比替代解决方案的单一组合。信息融合的关键是数据融合，它具有三个方面：（ⅰ）数据必须具有可比性和数值，并使用某种标准化过程； （ⅱ）必须考虑数据的不准确性； （ⅲ）必须选择适当的聚合函数，以将值组合成单个分数。最近，执行数据/信息融合的计算智能概念和技术正在作为合适的工具出现。尽管有着不同的观点，但在结合异构信息方面也已经进行了大量工作的另一个领域是多准则决策。通常，多准则问题是通过选择一组相关准则（通常是处理异构数据）来建模的，这些准则必须汇总（即融合）以获得每个候选备选方案的单一评级。在本文中，我们提出了一种数据/信息融合算法，其中包括来自多准则决策和计算智能的概念，特别是模糊多准则决策和具有加权功能的混​​合聚集算子。这项工作的应用领域是避免危险的航天器安全着陆。因此，将使用两个现有的危害图来说明该算法的多功能性。

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