Adaptive filtering with the self-organizing map: a performance comparison

Authors:
Guilherme A. Barreto;Luís Gustavo M. Souza
Affiliations:
Department of Teleinformatics Engineering, Federal University of Ceará, Center of Technology, Fortaleza, Ceará, Brazil;Department of Teleinformatics Engineering, Federal University of Ceará, Center of Technology, Fortaleza, Ceará, Brazil
Venue:
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Year:
2006

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Abstract

In this paper we provide an in-depth evaluation of the SOM as a feasible tool for nonlinear adaptive filtering. A comprehensive survey of existing SOM-based and related architectures for learning input-output mappings is carried out and the application of these architectures to nonlinear adaptive filtering is formulated. Then, we introduce two simple procedures for building RBF-based nonlinear filters using the Vector-Quantized Temporal Associative Memory (VQTAM), a recently proposed method for learning dynamical input-output mappings using the SOM. The aforementioned SOM-based adaptive filters are compared with standard FIR/LMS and FIR/LMS--Newton linear transversal filters, as well as with powerful MLP-based filters in nonlinear channel equalization and inverse modeling tasks. The obtained results in both tasks indicate that SOM-based filters can consistently outperform powerful MLP-based ones.