DocumentCode :
3117189
Title :
A Hybrid Unsupervised Clustering Algorithm for Channel Equalization
Author :
Knidel, Helder ; Ferrari, Rafael ; Duarte, Leonardo T. ; Suyama, Ricardo ; Attux, Romis R F ; De Castro, Leandro Nunes ; Von Zuben, Fernando José ; Romano, João Marcos T
Author_Institution :
Lab. of Bioinf. & Bio-inspired Comput. (LBiC), Univ. of Campinas (Unicamp), Campinas
fYear :
2006
fDate :
6-8 Sept. 2006
Firstpage :
459
Lastpage :
464
Abstract :
In this work, we propose and analyze the applicability of a novel unsupervised data clustering technique in the problem of channel equalization. The proposal combines two different methods, a neuro-immune network called RABNET [1] and the iterated local search algorithm (ILS) [2], to produce a tool that, in contrast to classical solutions like the k-means algorithm, does not require a priori knowledge about the number of clusters to be found and, moreover, possesses mechanisms to avoid local convergence. Simulation results attest both the viability and efficiency of the proposal in scenarios conceived to highlight certain aspects that can be decisive insofar as real-world applications are concerned.
Keywords :
equalisers; iterative methods; neural nets; pattern clustering; search problems; telecommunication computing; RABNET; channel equalization; data clustering; hybrid unsupervised clustering algorithm; iterated local search algorithm; k-means algorithm; neuro-immune network; real-valued antibody network; Bayesian methods; Bioinformatics; Biomedical signal processing; Blind equalizers; Clustering algorithms; Data engineering; Laboratories; Proposals; Signal analysis; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location :
Arlington, VA
ISSN :
1551-2541
Print_ISBN :
1-4244-0656-0
Electronic_ISBN :
1551-2541
Type :
conf
DOI :
10.1109/MLSP.2006.275594
Filename :
4053693
Link To Document :
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