基于相似度的模糊规则约简方法

2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) Pub Date : 2016-10-01 DOI:10.1109/NAFIPS.2016.7851603

Arturo Garcia-Garcia, M. Reformat, Andres Mendez-Vazquez

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引用次数: 2

摘要

模糊相似度量(FSMs)被广泛用于模糊集和模糊规则的比较。到目前为止，已经提出了许多不同的fsm。要确定一个最适合给定任务的FSM并不简单。在本文中，我们研究了一些FSMs对图像分割过程中规则数量减少问题的适用性。我们使用基于dirichlet的方法生成模糊集，这些模糊集进一步用于模糊if-then规则的构造。我们分析这些规则的相似性，并选择一定数量的规则用于图像分割。我们将这种方法应用于两幅不同的图像。

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Similarity-based method for reduction of fuzzy rules

Fuzzy Similarity Measures (FSMs) are widely used for comparison of fuzzy sets, as well as fuzzy rules. A multitude of different FSMs have been proposed so far. It is not straightforward to identify a single FSM that is the most suitable for a given task. In this paper, we investigate suitability of a few FSMs for the problem of reduction of number of rules for an image segmentation process. We use Dirichlet-based approach to generate fuzzy sets that are further used for construction of fuzzy if-then rules. We analyze similarity of these rules and select a specified number of rules for image segmentation purposes. We applied this approach to two different images.

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2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)

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