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Related Experiment Videos

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

Guilherme A Barreto1, Luís Gustavo M Souza

  • 1Department 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

Neural Networks : the Official Journal of the International Neural Network Society
|July 1, 2006
PubMed
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Self-Organizing Maps (SOMs) offer a powerful approach to nonlinear adaptive filtering. New RBF-based filters using Vector-Quantized Temporal Associative Memory (VQTAM) with SOMs outperform MLP-based filters in complex tasks.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Signal Processing

Background:

  • Nonlinear adaptive filtering is crucial for many signal processing applications.
  • Existing methods often struggle with complex nonlinear dynamics.
  • Self-Organizing Maps (SOMs) present a potential solution for learning input-output mappings.

Purpose of the Study:

  • To evaluate the Self-Organizing Map (SOM) as a tool for nonlinear adaptive filtering.
  • To introduce novel Radial Basis Function (RBF)-based nonlinear filters utilizing SOMs.
  • To compare the performance of SOM-based filters against established linear and neural network filters.

Main Methods:

  • A comprehensive survey of SOM-based architectures for learning input-output mappings.
  • Development of two RBF-based nonlinear filters using Vector-Quantized Temporal Associative Memory (VQTAM) with SOMs.

Related Experiment Videos

  • Comparative analysis against Finite Impulse Response/Least Mean Squares (FIR/LMS) and Multi-Layer Perceptron (MLP)-based filters.

    • Main Results:

    • SOM-based filters demonstrate effectiveness in nonlinear channel equalization.
    • These filters also show superior performance in inverse modeling tasks.
    • Performance of SOM-based filters consistently surpasses that of MLP-based filters.

    Conclusions:

    • SOMs are a feasible and high-performing tool for nonlinear adaptive filtering.
    • The proposed VQTAM-based SOM filters offer a significant advancement over existing methods.
    • SOM-based approaches provide a robust alternative for complex nonlinear signal processing challenges.