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Connecting Steiglitz-McBride Identification, Active Noise Control, and Coefficient Filtering to a Common Framework

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Model Identification and Adaptive Control

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Abstract

There are many adaptive algorithms that incoporate filtered versions of the regressor vector, the error signal, the parameter vector, or the update term. These modifications can often be viewed as attempts to recapture or improve the known stability properties of linear LMS algorithms, despite operating with IIR parameterizations or within a feedback loop. This chapter reviews some basic results on the (exponential) stability of adaptive algorithms, and then applies them in three situations: to the Steiglitz-McBride family of algorithms, to an analysis of algorithm performance in acoustic noise cancellation systems that usually incorporate the ‘filtered-u’ approach, and to a consolidation of coefficient filtering and update smoothing schemes such as momentum LMS and leakage. The principle of trading-off linear filters on various signals within the adaptive schemes is a powerful unifying principle that can be used to create ‘new’ algorithms, and it allows translation of stability results between seemingly unrelated algorithm forms.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering, Cornell University, Ithaca, NY, 14853, USA

    C. Richard Johnson Jr.

  2. Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, 53706, USA

    William A. Sethares

Authors
  1. C. Richard Johnson Jr.
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  2. William A. Sethares
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of Newcastle, Callaghan, 2308, NSW, Australia

    Graham Goodwin BSc, BE PhD, FIEEE, Hon FIE Aust., FTS, FAA

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© 2001 Springer-Verlag London Limited

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Johnson, C.R., Sethares, W.A. (2001). Connecting Steiglitz-McBride Identification, Active Noise Control, and Coefficient Filtering to a Common Framework. In: Goodwin, G. (eds) Model Identification and Adaptive Control. Springer, London. https://doi.org/10.1007/978-1-4471-0711-8_9

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  • DOI: https://doi.org/10.1007/978-1-4471-0711-8_9

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-1185-6

  • Online ISBN: 978-1-4471-0711-8

  • eBook Packages: Springer Book Archive

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