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Learning Fuzzy-Valued Fuzzy Measures for the Fuzzy-Valued Sugeno Fuzzy Integral

机译：模糊值Sugeno模糊积分的学习模糊值模糊测度

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摘要

Fuzzy integrals are very useful for fusing confidence or opinions from a variety of sources. These integrals are non-linear combinations of the support functions with the (possibly subjective) worth of subsets of the sources, realized by a fuzzy measure. There have been many applications and extensions of fuzzy integrals and this paper deals with a Sugeno integral where both the integrand and the measure take on fuzzy number values. A crucial aspect of using fuzzy integrals for fusion is determining or learning the measures. Here, we propose a genetic algorithm with novel cross-over and mutation operators to learn fuzzy-valued fuzzy measures for a fuzzy-valued Sugeno integral.
机译：模糊积分对于融合各种来源的信心或观点非常有用。这些积分是支持函数与源子集（可能是主观）的子集的非线性组合，通过模糊度量实现。模糊积分有许多应用和扩展，本文讨论了Sugeno积分，其中被积和测度均采用模糊数值。使用模糊积分进行融合的一个关键方面是确定或学习度量。在这里，我们提出了一种具有新颖的交叉和变异算子的遗传算法，以学习模糊值Sugeno积分的模糊值模糊测度。

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