Self-organizing map clustering based on continuous multiresolution entropy

被引：6

作者：

Torres, HM

Gurlekian, JA

Rufiner, HL

Torres, ME ^{[1
]}

机构：

[1] Univ Nacl Entre Rios, Fac Ingn, Lab Senales & Dinam Lineales, Oro Verde, Entre Rios, Argentina

[2] Hosp Clin Buenos Aires, Lab Invest Sensoriales, Consejo Nacl Invest Cient & Tecn, Inst Neurosciencias Aplicadas, Buenos Aires, DF, Argentina

[3] Univ Nacl Entre Rios, Fac Ingn, Lab Cibernet, Oro Verde, Entre Rios, Argentina

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2006年 / 361卷 / 01期

关键词：

wavelet transform; continuous multiresolution entropy; self-organizing maps; nonlinear systems; speech segmentation;

D O I：

10.1016/j.physa.2005.05.073

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

The detection of changes in the parameter values of a nonlinear dynamic system is a branch of study with multiple applications. in this paper, we explore a variant of an automatic detector and clustering of slight parameter variations in nonlinear dynamic systems proposed by Torres et al. [Automatic detection of slight changes in nonlinear dynamical systems using multiresolution entropy tools, Int. J. Bifure. Chaos 11(4) (2001) 967-981]. The new method takes the advantages of the continuous multiresolution entropy to localize slight changes ill the parameters, and uses self-organizing maps to quantify and cluster these changes. We discuss the performance of this method while applied to automatic segmentation of natural and synthetic diphthongs in the presence of additive noise. Our results show the potentiality of the proposed method. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：337 / 354

页数：18

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