NDLI: A fuzzy learning model for membership function estimation and pattern classification

Content Provider	IEEE Xplore Digital Library
Author	Fu-Lai Chung Tong Lee
Copyright Year	1994
Description	Author affiliation: Dept. of Electron. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong (Fu-Lai Chung; Tong Lee)
Abstract	In this paper, a new fuzzy learning model called fuzzy learning vector quantization (FLVQ) which incorporates fuzzy clustering concept with Kohonen's learning vector quantization (LVQ) model is proposed. The new learning algorithm is derived from optimizing an appropriate fuzzy objective function which takes into account two goals, namely, minimizing the differences between target and actual class membership outputs, and minimizing the distances between training patterns and the neuron's parametric vectors. It retains the LVQ's reinforce-or-punish learning principle and more importantly introduces graded corrections. As compared with the LVQ algorithm, the proposed one is characterized by several distinctive features: i) avoiding neuron underutilization; ii) superior classification and generalization performances; and iii) insensitive to initial conditions. Since the outputs of the model have been formulated as fuzzy class membership functions, it can be readily used to estimate the membership functions of fuzzy systems. Furthermore, through the concept of "clusters as rules", the trained network can be interpreted in the form of fuzzy IF-THEN rules. All these features of the proposed model are demonstrated through numerical examples.<>
Starting Page	426
Ending Page	431
File Size	482322
Page Count	6
File Format	PDF
ISBN	078031896X
DOI	10.1109/FUZZY.1994.343748
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern classification Neurons Vector quantization Clustering algorithms Fuzzy systems Neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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