Recurrent radial basis function networks for adaptive noise cancellation

Radial basis function neural network architectures are introduced for the nonlinear adaptive noise cancellation problem. Both FIR and IIR filter designs are considered, and it is shown that by exploiting the duality with system identification, the nonlinear IIR filter can be configured as a recurren...

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Bibliographic Details
Published in:Neural networks 1995-01, Vol.8 (2), p.273-290
Main Authors: Billings, Steve A., Fling, Chi F.
Format: Article
Language:English
Subjects:
Applied sciences
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
Noise cancellation
Nonlinear filtering
Radial basis function network
Signal and communications theory
Signal, noise
Telecommunications and information theory
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Summary:Radial basis function neural network architectures are introduced for the nonlinear adaptive noise cancellation problem. Both FIR and IIR filter designs are considered, and it is shown that by exploiting the duality with system identification, the nonlinear IIR filter can be configured as a recurrent radial basis function network. Details of network training that is based on a combined k-means clustering and Givens routine, the inclusion of linear dynamic network links, and metrics for performance monitoring are also discussed. Examples are included to demonstrate the degree of noise suppression that can be achieved based on the new design.
ISSN:0893-6080
1879-2782
DOI:10.1016/0893-6080(94)00078-Z