An Experimental Assessment of Transverse Adaptive Fir Filters as Applied to Vibrating Structures Identification
The present work is aimed at assessing the performance of adaptive Finite Impulse Response ( FIR ) filters on the identification of vibrating structures. Four adaptive algorithms were used: Least Mean Squares ( LMS ), Normalized Least Mean Squares ( NLMS ), Transform‐Domain Least Mean Squares ( TD –...
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| Published in | Shock and vibration Vol. 12; no. 3; pp. 197 - 216 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Wiley
01.01.2005
|
| Online Access | Get full text |
| ISSN | 1070-9622 1875-9203 1875-9203 |
| DOI | 10.1155/2005/917832 |
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| Abstract | The present work is aimed at assessing the performance of adaptive Finite Impulse Response (
FIR
) filters on the identification of vibrating structures. Four adaptive algorithms were used: Least Mean Squares (
LMS
), Normalized Least Mean Squares (
NLMS
), Transform‐Domain Least Mean Squares (
TD
–
LMS
) and Set‐Membership Binormalized Data‐Reusing
LMS
Algorithm (
SM
–
BNDRLMS
). The capability of these filters to perform the identification of vibrating structures is shown on real experiments. The first experiment consists of an aluminum cantilever beam containing piezoelectric sensors and actuators and the second one is a steel pinned‐pinned beam instrumented with accelerometers and an electromechanical shaker. |
|---|---|
| AbstractList | The present work is aimed at assessing the performance of adaptive Finite Impulse Response (FIR) filters on the identification of vibrating structures. Four adaptive algorithms were used: Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS), Transform-Domain Least Mean Squares (TD-LMS) and Set-Membership Binormalized Data-Reusing LMS Algorithm (SM-BNDRLMS). The capability of these filters to perform the identification of vibrating structures is shown on real experiments. The first experiment consists of an aluminum cantilever beam containing piezoelectric sensors and actuators and the second one is a steel pinned-pinned beam instrumented with accelerometers and an electromechanical shaker. The present work is aimed at assessing the performance of adaptive Finite Impulse Response ( FIR ) filters on the identification of vibrating structures. Four adaptive algorithms were used: Least Mean Squares ( LMS ), Normalized Least Mean Squares ( NLMS ), Transform‐Domain Least Mean Squares ( TD – LMS ) and Set‐Membership Binormalized Data‐Reusing LMS Algorithm ( SM – BNDRLMS ). The capability of these filters to perform the identification of vibrating structures is shown on real experiments. The first experiment consists of an aluminum cantilever beam containing piezoelectric sensors and actuators and the second one is a steel pinned‐pinned beam instrumented with accelerometers and an electromechanical shaker. |
| Author | Castello, Daniel A. Rochinha, Fernando A. |
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FIR
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| Title | An Experimental Assessment of Transverse Adaptive Fir Filters as Applied to Vibrating Structures Identification |
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