Fuzzy adaptive multi-module approximation network

Wonil Kim, Kishan Mehrotra, Chilukuri K Mohan

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper presents a fuzzy version of the Adaptive Multi-module Approximation Network. New modules are generated when performance of existing modules is inadequate for some training data, and the applicability of a module to each input vector is determined based on the fuzzy membership of that vector in the possibly asymmetric clusters represented by the reference vectors associated with different modules. The main idea is that for neural networks that rely on a reference vector (for vector quantization, clustering, and similar tasks), the use of fuzzy membership criterion based on the distribution of data (inside different Voronoi cells) may be more appropriate than the traditional approach using a Euclidean metric to determine to which cell each data point belongs.

Original languageEnglish (US)
Title of host publicationAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS
PublisherIEEE Computer Society
Pages615-619
Number of pages5
StatePublished - 1999
EventProceedings of the 1999 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS'99 - New York, NY, USA
Duration: Jun 10 1999Jun 12 1999

Other

OtherProceedings of the 1999 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS'99
CityNew York, NY, USA
Period6/10/996/12/99

ASJC Scopus subject areas

  • Computer Science(all)
  • Media Technology

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