NDLI: Some applications of an asymmetric subsethood product fuzzy neural inference system

Content Provider	IEEE Xplore Digital Library
Author	Shunmuga Velayutham, C. Kumar, S.
Copyright Year	2003
Description	Author affiliation: Dept. of Phys. & Comput. Sci., Dayalbagh Educ. Inst., Agra, India (Shunmuga Velayutham, C.; Kumar, S.)
Abstract	This paper presents some applications of an asymmetric subsethood product fuzzy neural inference system (ASuP-FuNIS). The ASuPFuNIS model extends SuPFuNIS by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood product network admits both numeric as well as linguistic inputs. Numeric inputs are fuzzified prior to their application to the network; linguistic inputs are presented without modification. The network architecture directly embeds fuzzy if-then rules, and connections represent antecedent and consequent fuzzy sets. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. The model is economical in terms of the number of rules required to solve difficult problems and is robust against random variations in data sets. Simulation results on three benchmark problems-the Hepatitis diagnosis, Iris data classification and the Narazaki-Ralescu function approximation problem-show that the subsethood based model performs excellently with minimal number of rules.
Sponsorship	IEEE IEEE Neurla Networks Soc
Starting Page	202
Ending Page	207
File Size	426372
Page Count	6
File Format	PDF
ISBN	0780378105
DOI	10.1109/FUZZ.2003.1209362
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Fuzzy sets Fuzzy neural networks Function approximation Iris Fuzzy control Application software Liver diseases Physics Computer science
Content Type	Text
Resource Type	Article

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