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Interval type-2 fuzzy membership function generation methods for pattern recognition. (English) Zbl 1178.68488
Summary: Type-2 Fuzzy Sets (T2 FSs) have been shown to manage uncertainty more effectively than T1 Fuzzy Sets (T1 FSs) in several areas of engineering. However, computing with T2 FSs can require undesirably large amount of computations since it involves numerous embedded T2 FSs. To reduce the complexity, interval type-2 fuzzy sets can be used, since the secondary memberships are all equal to one. In this paper, three novel interval type-2 fuzzy membership function generation methods are proposed. The methods are based on heuristics, histograms, and interval type-2 fuzzy \(C\)-means. The performance of the methods is evaluated by applying them to back-propagation neural networks. Experimental results for several data sets are given to show the effectiveness of the proposed membership assignments.

MSC:
68T10 Pattern recognition, speech recognition
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