title: Adaptive filtering with the self-organizing map: a performance comparison.
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.
authors.affiliation: Department of Teleinformatics Engineering, Federal University of Ceará, Av. Mister Hull, S/N-C.P. 6005, CEP 60455-760, Center of Technology, Campus do Pici, Fortaleza, Ceará, Brazil. guilherme@deti.ufc.br
authors.forename: Guilherme A
authors.initials: GA
authors.lastname: Barreto
authors.forename: Luís Gustavo M
authors.initials: LG
authors.lastname: Souza
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics