Library

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Electronic Resource
    Electronic Resource
    Tracking the condition number for RLS in signal processing (1992)
    Springer
    Mathematics of control, signals, and systems 5 (1992), S. 23-39 
    Details
    ISSN: 1435-568X
    Keywords: Adaptive algorithms ; Condition estimation ; Recursive least squares ; Signal processing ; Singular values
    Source: Springer Online Journal Archives 1860-2000
    Topics: Electrical Engineering, Measurement and Control Technology , Mathematics , Technology
    Notes: Abstract We apply a fast adaptive condition estimation scheme, calledACE, to recursive least squares (RLS) computations in signal processing.ACE is fast in the sense that onlyO(n) operations are required forn parameter problems, and is adaptive over time, i.e., estimates at timet are used to produce estimates at timet + 1. RLS algorithms for linear prediction of time series are applied in various fields of signal processing: identification, estimation, and control. However, RLS algorithms are known to suffer from numerical instability problems under finite word-length conditions, due to ill-conditioning. We apply adaptive procedures, linear in the order of the problem, for accurately tracking relevant extreme eigen-values or singular values and the associated condition numbers over timet. In this paper exponentially weighted data windows are considered. The sliding data window case, which involves downdating as well as updating, is considered else-where. Numerical experiments indicate thatACE yields an accurate, yet inexpensive, RLS condition estimator for signal processing applications.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01211974
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Adaptive Lanczos methods for recursive condition estimation (1991)
    Springer
    Numerical algorithms 1 (1991), S. 1-19 
    Details
    ISSN: 1572-9265
    Keywords: AMS(MOS) ; 15A18 ; 65F10 ; 65F20 ; 65F35 ; Adaptive methods ; condition estimation ; control ; downdating ; eigenvalues ; Lanczos methods ; matrix modifications ; recursive least squares ; signal processing ; singular values ; updating
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Estimates for the condition number of a matrix are useful in many areas of scientific computing, including: recursive least squares computations, optimization, eigenanalysis, and general nonlinear problems solved by linearization techniques where matrix modification techniques are used. The purpose of this paper is to propose anadaptiveLanczosestimator scheme, which we callale, for tracking the condition number of the modified matrix over time. Applications to recursive least squares (RLS) computations using the covariance method with sliding data windows are considered.ale is fast for relatively smalln-parameter problems arising in RLS methods in control and signal processing, and is adaptive over time, i.e., estimates at timet are used to produce estimates at timet+1. Comparisons are made with other adaptive and non-adaptive condition estimators for recursive least squares problems. Numerical experiments are reported indicating thatale yields a very accurate recursive condition estimator.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02145580
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...