Optimizing the performance of polynomial adaptive filters: making quadratic filters converge like linear filters

Charles W. Therrien; W. Kenneth Jenkins; Xiaohui Li

doi:10.1109/78.752619

Optimizing the performance of polynomial adaptive filters: making quadratic filters converge like linear filters

Charles W. Therrien
, W. Kenneth Jenkins
, Xiaohui Li

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

The correlation properties of the input vector determine the rate of convergence of the LMS algorithm for Volterra adaptive filters and are optimal when the nonlinear input terms are uncorrelated. This correspondence presents new results on the correlation properties for second-order Volterra filters and shows that when the input signal is whitened, the nonlinear terms automatically become uncorrelated.

Original language	English (US)
Pages (from-to)	1169-1171
Number of pages	3
Journal	IEEE Transactions on Signal Processing
Volume	47
Issue number	4
DOIs	https://doi.org/10.1109/78.752619
State	Published - Apr 1999

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering

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10.1109/78.752619

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title = "Optimizing the performance of polynomial adaptive filters: making quadratic filters converge like linear filters",

abstract = "The correlation properties of the input vector determine the rate of convergence of the LMS algorithm for Volterra adaptive filters and are optimal when the nonlinear input terms are uncorrelated. This correspondence presents new results on the correlation properties for second-order Volterra filters and shows that when the input signal is whitened, the nonlinear terms automatically become uncorrelated.",

author = "Therrien, \{Charles W.\} and Jenkins, \{W. Kenneth\} and Xiaohui Li",

note = "Funding Information: Manuscript received February 28, 1997; revised March 6, 1998. This work was sponsored by the Joint Services Electronics Program (JSEP) under Contract N00014-90-J-1270 and by the Naval Postgraduate School Direct Funded Research Program. The opinions expressed in this work do not necessarily represent those of the sponsoring agencies. The associate editor coordinating the review of this paper and approving it for publication was Prof. Chi Chung Ko.",

year = "1999",

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doi = "10.1109/78.752619",

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AU - Jenkins, W. Kenneth

AU - Li, Xiaohui

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PY - 1999/4

Y1 - 1999/4

N2 - The correlation properties of the input vector determine the rate of convergence of the LMS algorithm for Volterra adaptive filters and are optimal when the nonlinear input terms are uncorrelated. This correspondence presents new results on the correlation properties for second-order Volterra filters and shows that when the input signal is whitened, the nonlinear terms automatically become uncorrelated.

AB - The correlation properties of the input vector determine the rate of convergence of the LMS algorithm for Volterra adaptive filters and are optimal when the nonlinear input terms are uncorrelated. This correspondence presents new results on the correlation properties for second-order Volterra filters and shows that when the input signal is whitened, the nonlinear terms automatically become uncorrelated.

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ER -

Optimizing the performance of polynomial adaptive filters: making quadratic filters converge like linear filters

Abstract

All Science Journal Classification (ASJC) codes

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