A Fast Lane Approach to LMS prediction of respiratory motion signals

Floris Ernst, Alexander Schlaefer, Sonja Dieterich, Achim Schweikard

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

As a tool for predicting stationary signals, the Least Mean Squares (LMS) algorithm is widely used. Its improvement, the family of normalised LMS algorithms, is known to outperform this algorithm. However, they still remain sensitive to selecting wrong parameters, being the learning coefficient μ and the signal history length M. We propose an improved version of both algorithms using a Fast Lane Approach, based on parallel evaluation of several competing predictors. These were applied to respiratory motion data from motion-compensated radiosurgery. Prediction was performed using arbitrarily selected values for the learning coefficient μ ∈] 0, 0.3] and the signal history length M ∈ [1, 15]. The results were compared to prediction using the globally optimal values of μ and M found using a grid search. When the learning algorithm is seeded using locally optimal values (found using a grid search on the first 96 s of data), more than 44% of the test cases outperform the globally optimal result. In about 38% of the cases, the result comes to within 5% and, in about 9% of the cases, to within 5-10% of the global optimum. This indicates that the Fast Lane Approach is a robust method for selecting the parameters μ and M.

Original languageEnglish (US)
Pages (from-to)291-299
Number of pages9
JournalBiomedical Signal Processing and Control
Volume3
Issue number4
DOIs
StatePublished - Oct 2008
Externally publishedYes

Fingerprint

Least-Squares Analysis
Learning
History
Radiosurgery
Learning algorithms

Keywords

  • LMS
  • Parameter selection
  • Radiosurgery
  • Respiratory motion prediction

ASJC Scopus subject areas

  • Health Informatics
  • Signal Processing

Cite this

A Fast Lane Approach to LMS prediction of respiratory motion signals. / Ernst, Floris; Schlaefer, Alexander; Dieterich, Sonja; Schweikard, Achim.

In: Biomedical Signal Processing and Control, Vol. 3, No. 4, 10.2008, p. 291-299.

Research output: Contribution to journalArticle

Ernst, Floris ; Schlaefer, Alexander ; Dieterich, Sonja ; Schweikard, Achim. / A Fast Lane Approach to LMS prediction of respiratory motion signals. In: Biomedical Signal Processing and Control. 2008 ; Vol. 3, No. 4. pp. 291-299.
@article{af0a85dde6ff4b71927fa5cf5c69f36c,
title = "A Fast Lane Approach to LMS prediction of respiratory motion signals",
abstract = "As a tool for predicting stationary signals, the Least Mean Squares (LMS) algorithm is widely used. Its improvement, the family of normalised LMS algorithms, is known to outperform this algorithm. However, they still remain sensitive to selecting wrong parameters, being the learning coefficient μ and the signal history length M. We propose an improved version of both algorithms using a Fast Lane Approach, based on parallel evaluation of several competing predictors. These were applied to respiratory motion data from motion-compensated radiosurgery. Prediction was performed using arbitrarily selected values for the learning coefficient μ ∈] 0, 0.3] and the signal history length M ∈ [1, 15]. The results were compared to prediction using the globally optimal values of μ and M found using a grid search. When the learning algorithm is seeded using locally optimal values (found using a grid search on the first 96 s of data), more than 44{\%} of the test cases outperform the globally optimal result. In about 38{\%} of the cases, the result comes to within 5{\%} and, in about 9{\%} of the cases, to within 5-10{\%} of the global optimum. This indicates that the Fast Lane Approach is a robust method for selecting the parameters μ and M.",
keywords = "LMS, Parameter selection, Radiosurgery, Respiratory motion prediction",
author = "Floris Ernst and Alexander Schlaefer and Sonja Dieterich and Achim Schweikard",
year = "2008",
month = "10",
doi = "10.1016/j.bspc.2008.06.001",
language = "English (US)",
volume = "3",
pages = "291--299",
journal = "Biomedical Signal Processing and Control",
issn = "1746-8094",
publisher = "Elsevier BV",
number = "4",

}

TY - JOUR

T1 - A Fast Lane Approach to LMS prediction of respiratory motion signals

AU - Ernst, Floris

AU - Schlaefer, Alexander

AU - Dieterich, Sonja

AU - Schweikard, Achim

PY - 2008/10

Y1 - 2008/10

N2 - As a tool for predicting stationary signals, the Least Mean Squares (LMS) algorithm is widely used. Its improvement, the family of normalised LMS algorithms, is known to outperform this algorithm. However, they still remain sensitive to selecting wrong parameters, being the learning coefficient μ and the signal history length M. We propose an improved version of both algorithms using a Fast Lane Approach, based on parallel evaluation of several competing predictors. These were applied to respiratory motion data from motion-compensated radiosurgery. Prediction was performed using arbitrarily selected values for the learning coefficient μ ∈] 0, 0.3] and the signal history length M ∈ [1, 15]. The results were compared to prediction using the globally optimal values of μ and M found using a grid search. When the learning algorithm is seeded using locally optimal values (found using a grid search on the first 96 s of data), more than 44% of the test cases outperform the globally optimal result. In about 38% of the cases, the result comes to within 5% and, in about 9% of the cases, to within 5-10% of the global optimum. This indicates that the Fast Lane Approach is a robust method for selecting the parameters μ and M.

AB - As a tool for predicting stationary signals, the Least Mean Squares (LMS) algorithm is widely used. Its improvement, the family of normalised LMS algorithms, is known to outperform this algorithm. However, they still remain sensitive to selecting wrong parameters, being the learning coefficient μ and the signal history length M. We propose an improved version of both algorithms using a Fast Lane Approach, based on parallel evaluation of several competing predictors. These were applied to respiratory motion data from motion-compensated radiosurgery. Prediction was performed using arbitrarily selected values for the learning coefficient μ ∈] 0, 0.3] and the signal history length M ∈ [1, 15]. The results were compared to prediction using the globally optimal values of μ and M found using a grid search. When the learning algorithm is seeded using locally optimal values (found using a grid search on the first 96 s of data), more than 44% of the test cases outperform the globally optimal result. In about 38% of the cases, the result comes to within 5% and, in about 9% of the cases, to within 5-10% of the global optimum. This indicates that the Fast Lane Approach is a robust method for selecting the parameters μ and M.

KW - LMS

KW - Parameter selection

KW - Radiosurgery

KW - Respiratory motion prediction

UR - http://www.scopus.com/inward/record.url?scp=50249185259&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50249185259&partnerID=8YFLogxK

U2 - 10.1016/j.bspc.2008.06.001

DO - 10.1016/j.bspc.2008.06.001

M3 - Article

AN - SCOPUS:50249185259

VL - 3

SP - 291

EP - 299

JO - Biomedical Signal Processing and Control

JF - Biomedical Signal Processing and Control

SN - 1746-8094

IS - 4

ER -