NDLI: Neural approach to linguistic approximation of fuzzy sets

Content Provider	IEEE Xplore Digital Library
Author	Zahan, S.
Copyright Year	2001
Description	Author affiliation: Dept. of Commun., Tech. Univ. of Cluj-Napoca, Romania (Zahan, S.)
Abstract	In many practical implementations involving fuzzy systems, a numerical value is delivered to the user as the output of the system. Nevertheless, there are applications where such a numeric value is of little interest for the user, which, in turn, demands a linguistic output. In these cases, the linguistic approximation of the output has to be performed. This is not a simple problem, since often the application of approximate reasoning leads to very irregular non-normal, possibly non-convex output fuzzy sets. This paper shows that linguistic approximation can be approached in a more traditional way as a problem of compatibility between fuzzy sets, but also as a pattern classification problem. Several possible solutions are presented and discussed. Emphasis is put on a neural-based pattern classification approach. In order to comparatively evaluate the performances of all considered solutions, each one was integrated within an expert system dedicated to coronary heart disease diagnosis. The performance of the whole system was then evaluated using clinical data from 152 patients, by comparing the linguistic output of the expert system to the results of the gold standard of coronary angiography.
Sponsorship	Int. Fuzzy Syst. Assoc.
Starting Page	2693
Ending Page	2697
File Size	593243
Page Count	5
File Format	PDF
ISBN	0780370783
DOI	10.1109/NAFIPS.2001.943649
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-25
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Performance evaluation Fuzzy systems Diagnostic expert systems Cardiac disease Gold Angiography Qualifications Hybrid intelligent systems Degradation
Content Type	Text
Resource Type	Article

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