Fuzzy learning vector quantization

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

15 Citations (Scopus)

Abstract

In this paper, a new supervised competitive learning network model called fuzzy learning vector quantization (FLVQ) which incorporates fuzzy concepts into the learning vector quantization (LVQ) networks is proposed. Unlike the original algorithm, the FLVQ's learning algorithm is derived from optimizing an appropriate fuzzy objective function which takes into accounts of two goals, namely, minimizing the network output error which is the class membership differences of target and actual values and minimizing the distances between training patterns and competing neurons. As compared with the LVQ network, the proposed one consists of several distinctive features: i) stand-alone operation, i.e. employing preprocessing algorithm to obtain a good initial network state is not required; ii) superior classification performance, particularly in overlapping data sets; and iii) avoiding neuron underutilization. These advantages are demonstrated through an artificially generated data set and a vowel recognition data set.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages2739-2742
Number of pages4
Volume3
ISBN (Print)0780314212, 9780780314214
Publication statusPublished - 1 Dec 1993
Externally publishedYes
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Japan
Duration: 25 Oct 199329 Oct 1993

Conference

ConferenceProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
Country/TerritoryJapan
CityNagoya
Period25/10/9329/10/93

ASJC Scopus subject areas

  • Engineering(all)

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