Computationally efficient algorithm for adaptive quadratic Volterra filters

Xiaohui Li; William Kenneth Jenkins; Charles W. Therrien

Computationally efficient algorithm for adaptive quadratic Volterra filters

Xiaohui Li, William Kenneth Jenkins, Charles W. Therrien

Research output: Contribution to journal › Conference article › peer-review

Abstract

The structure of the input autocorrelation matrix in Volterra second order adaptive filters for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. It is shown that when the input signal samples are ordered properly within the input data vector, the autocorrelation matrix of quadratic filter inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms are presented that take advantage of the sparsity and unique structure of the correlation matrix that results from this formulation.

Original language	English (US)
Pages (from-to)	2184-2187
Number of pages	4
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	4
State	Published - Jan 1 1997
Event	Proceedings of the 1997 IEEE International Symposium on Circuits and Systems, ISCAS'97. Part 4 (of 4) - Hong Kong, Hong Kong Duration: Jun 9 1997 → Jun 12 1997

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Cite this

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title = "Computationally efficient algorithm for adaptive quadratic Volterra filters",

abstract = "The structure of the input autocorrelation matrix in Volterra second order adaptive filters for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. It is shown that when the input signal samples are ordered properly within the input data vector, the autocorrelation matrix of quadratic filter inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms are presented that take advantage of the sparsity and unique structure of the correlation matrix that results from this formulation.",

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

year = "1997",

month = jan,

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language = "English (US)",

volume = "4",

pages = "2184--2187",

journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

issn = "0271-4310",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Proceedings of the 1997 IEEE International Symposium on Circuits and Systems, ISCAS'97. Part 4 (of 4) ; Conference date: 09-06-1997 Through 12-06-1997",

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T1 - Computationally efficient algorithm for adaptive quadratic Volterra filters

AU - Li, Xiaohui

AU - Jenkins, William Kenneth

AU - Therrien, Charles W.

PY - 1997/1/1

Y1 - 1997/1/1

N2 - The structure of the input autocorrelation matrix in Volterra second order adaptive filters for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. It is shown that when the input signal samples are ordered properly within the input data vector, the autocorrelation matrix of quadratic filter inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms are presented that take advantage of the sparsity and unique structure of the correlation matrix that results from this formulation.

AB - The structure of the input autocorrelation matrix in Volterra second order adaptive filters for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. It is shown that when the input signal samples are ordered properly within the input data vector, the autocorrelation matrix of quadratic filter inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms are presented that take advantage of the sparsity and unique structure of the correlation matrix that results from this formulation.

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UR - http://www.scopus.com/inward/citedby.url?scp=0030708407&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0030708407

SN - 0271-4310

VL - 4

SP - 2184

EP - 2187

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

T2 - Proceedings of the 1997 IEEE International Symposium on Circuits and Systems, ISCAS'97. Part 4 (of 4)

Y2 - 9 June 1997 through 12 June 1997

ER -

Computationally efficient algorithm for adaptive quadratic Volterra filters

Abstract

All Science Journal Classification (ASJC) codes

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