Alternate Realizations to Adaptive IIR Filters and Properties of Their Performance Surfaces

Majid Nayeri; W. Kenneth Jenkins

doi:10.1109/31.92881

Alternate Realizations to Adaptive IIR Filters and Properties of Their Performance Surfaces

Majid Nayeri, W. Kenneth Jenkins

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

51 Scopus citations

Abstract

Characteristics of the mean-square error surface in adaptive digital filters determine how well a gradient algorithm performs within a given filter structure, i.e., if the surface has steep slopes and contains local minima, a gradient algorithm will have difficulty reaching the global minimum. It is shown how different filter structures of an adaptive filter leads to a change in the characteristics of the corresponding error surface, and consequently, to a change in the corresponding convergence rate and minimum mean-square error.

Original language	English (US)
Pages (from-to)	485-496
Number of pages	12
Journal	IEEE Transactions on Circuits and Systems
Volume	36
Issue number	4
DOIs	https://doi.org/10.1109/31.92881
State	Published - Apr 1989

All Science Journal Classification (ASJC) codes

General Engineering

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10.1109/31.92881

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abstract = "Characteristics of the mean-square error surface in adaptive digital filters determine how well a gradient algorithm performs within a given filter structure, i.e., if the surface has steep slopes and contains local minima, a gradient algorithm will have difficulty reaching the global minimum. It is shown how different filter structures of an adaptive filter leads to a change in the characteristics of the corresponding error surface, and consequently, to a change in the corresponding convergence rate and minimum mean-square error.",

author = "Majid Nayeri and Jenkins, {W. Kenneth}",

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N2 - Characteristics of the mean-square error surface in adaptive digital filters determine how well a gradient algorithm performs within a given filter structure, i.e., if the surface has steep slopes and contains local minima, a gradient algorithm will have difficulty reaching the global minimum. It is shown how different filter structures of an adaptive filter leads to a change in the characteristics of the corresponding error surface, and consequently, to a change in the corresponding convergence rate and minimum mean-square error.

AB - Characteristics of the mean-square error surface in adaptive digital filters determine how well a gradient algorithm performs within a given filter structure, i.e., if the surface has steep slopes and contains local minima, a gradient algorithm will have difficulty reaching the global minimum. It is shown how different filter structures of an adaptive filter leads to a change in the characteristics of the corresponding error surface, and consequently, to a change in the corresponding convergence rate and minimum mean-square error.

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Alternate Realizations to Adaptive IIR Filters and Properties of Their Performance Surfaces

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