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

共 50 条

[21] A Deep Clustering Algorithm Based on Self-organizing Map Neural Network
Tao, Yanling
Li, Ying
Lin, Xianghong
[J]. INTELLIGENT COMPUTING METHODOLOGIES, ICIC 2018, PT III, 2018, 10956 : 182 - 192
[22] Clustering the self-organizing map based on the neurons' associated pattern sets
Brito da Silva, Leonardo Enzo
Ferreira Costa, Jose Alfredo
[J]. 2013 1ST BRICS COUNTRIES CONGRESS ON COMPUTATIONAL INTELLIGENCE AND 11TH BRAZILIAN CONGRESS ON COMPUTATIONAL INTELLIGENCE (BRICS-CCI & CBIC), 2013, : 7 - 14
[23] A clustering based on Self-Organizing Map and knowledge discovery by neural network
Nakagawa, K
Kamiura, N
Hata, Y
[J]. NEW PARADIGM OF KNOWLEDGE ENGINEERING BY SOFT COMPUTING, 2001, 5 : 273 - 296
[24] A clustering method considering continuity of data based on self-organizing map
Imamura, Hiroki
Fujimura, Makoto
Kuroda, Hideo
[J]. Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers, 2006, 60 (08): : 1312 - 1316
[25] Topology-Based Clustering Using Polar Self-Organizing Map
Xu, Lu
Chow, Tommy W. S.
Ma, Eden W. M.
[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (04) : 798 - 808
[26] Component-based visual clustering using the self-organizing map
Hussain, Mustaq
Eakins, John P.
[J]. NEURAL NETWORKS, 2007, 20 (02) : 260 - 273
[27] Clustering algorithm based on particle swarm optimization and self-organizing map
Tang, Xianlun
Qiu, Guoqing
Li, Yinguo
Cao, Changxiu
[J]. Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2007, 35 (05): : 31 - 33
[28] TCSOM: Clustering transactions using self-organizing map
He, ZY
Xu, XF
Deng, SC
[J]. NEURAL PROCESSING LETTERS, 2005, 22 (03) : 249 - 262
[29] Self-organizing map for clustering of remote sensing imagery
Stoica, Radu-Mihai
Neagoe, Victor-Emil
[J]. 1600, (76): : 69 - 80
[30] Self-organizing map clustering analysis for molecular data
Wang, Lin
Jiang, Minghu
Lu, Yinghua
Noe, Frank
Smith, Jeremy C.
[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1, 2006, 3971 : 1250 - 1255

← 1 2 3 4 5 →