Fuzzy learning vector quantization generation of codebooks

Author

Wong, T. ; Gargour, C.S. ; Batani, N.

Author_Institution

Ecole de Technol. Superieure, Quebec Univ., Montreal, Que., Canada

Volume

2

fYear

1995

fDate

5-8 Sep 1995

Firstpage

1180

Abstract

A modification of the learning vector quantization (LVQ) method used for the generation of codebooks is presented. It retains the simplicity of the LVQ method while eliminating the non uniform spatial distribution of the prototype vectors which could result from an inadequate choice of the input signal sequence and/or from the initial choice of the prototype vectors. The method is based upon the segmentation of the input vector space in fuzzy partitions. A fuzzy objective function is defined. An algorithm for its minimization is presented. Simulation results are given

Keywords

fuzzy neural nets; learning (artificial intelligence); minimisation; self-organising feature maps; vector quantisation; algorithm; codebooks; fuzzy learning vector quantization generation; fuzzy objective function; fuzzy partitions; input signal sequence; input vector space segmentation; minimization; prototype vectors; simulation; Clouds; Content addressable storage; Minimization methods; Nonlinear filters; Partitioning algorithms; Probability density function; Prototypes; Signal processing; Statistical distributions; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 1995. Canadian Conference on

Conference_Location

Montreal, Que.

ISSN

0840-7789

Print_ISBN

0-7803-2766-7

Type

conf

DOI

10.1109/CCECE.1995.526673

Filename

526673