Adaptive Noise Cancellation Using Recurrent Radial Basis Function Networks
Billings, S.A. and Fung, C.F.
(1993)
Adaptive Noise Cancellation Using Recurrent Radial Basis Function Networks.
Research Report.
ACSE Research Report 472
.
Department of Automatic Control and Systems Engineering
Abstract
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 that the nonlinear IIR filter can be configured as a recurrent radial basis function network. Details of network training which 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.
Metadata
Item Type:
Monograph
Authors/Creators:
Billings, S.A.
Fung, C.F.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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