Actas de congresos
A Hybrid Unsupervised Clustering Algorithm For Channel Equalization
Registro en:
1424406560; 9781424406562
Proceedings Of The 2006 16th Ieee Signal Processing Society Workshop On Machine Learning For Signal Processing, Mlsp 2006. , v. , n. , p. 459 - 464, 2007.
10.1109/MLSP.2006.275594
2-s2.0-38949191934
Autor
Knidel H.
Ferrari R.
Duarte L.T.
Suyama R.
Attux R.R.F.
De Castro L.N.
Von Zuben F.J.
Romano J.M.T.
Institución
Resumen
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. © 2006 IEEE.
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