NDLI: The Association between Non-Stationary and Interval Type-2 Fuzzy Sets: A Case Study

Content Provider	IEEE Xplore Digital Library
Author	Garibaldi, J.M. Musikasuwan, S. Ozen, T.
Copyright Year	2005
Description	Author affiliation: Automated Scheduling, Optimisation & Planning Group, Nottingham Univ. (Garibaldi, J.M.; Musikasuwan, S.; Ozen, T.)
Abstract	In this paper a notion termed nonstationary fuzzy sets is introduced and the concept of random perturbations that can be used for generating these nonstationary fuzzy sets is also presented. A case study was carried out to investigate the relationship between the performance of nonstationary fuzzy logic systems and interval type-2 fuzzy logic systems. It can be observed that in case of centre variation, the lower-upper boundaries of outputs predicted by nonstationary systems are slightly narrower than those from the corresponding interval type-2 systems. On the other hand, in case of width variation, the lower-upper boundaries of outputs predicted by nonstationary systems are slightly wider than those from the type-2 systems. Moreover, an interesting observation is that the secondary membership function of the type-2 sets corresponding to nonstationary fuzzy sets generated using normally distributed perturbations are nonuniform. In contrast to noninterval type-2 sets, this does not affect the inference process of the nonstationary sets. In this sense, the use of nonstationary fuzzy sets may enable approximations to be made of general type-2 fuzzy inferencing
Sponsorship	IEEE IEEE Neural Networks Soc
Starting Page	224
Ending Page	229
File Size	2190367
Page Count	6
File Format	PDF
ISBN	0780391594
DOI	10.1109/FUZZY.2005.1452397
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Computer aided software engineering Uncertainty Humans Fuzzy logic Computational complexity Inference algorithms Decision making Expert systems Hybrid intelligent systems
Content Type	Text
Resource Type	Article

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