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S. Haykin and B. Widrow, “Least-Mean-Square Adaptive Filters (Adaptive and Learning Systems for Signal Processing, Communications and Control),” Wiley Interscience, 2003.

has been cited by the following article:

• TITLE: FIR System Identification Using Feedback

  AUTHORS: T. J. Moir

  KEYWORDS: LMS; Feedback; System Identification

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.4 No.4, November 1, 2013

  ABSTRACT: This paper describes a new approach to finite-impulse (FIR) system identification. The method differs from the traditional stochastic approximation method as used in the traditional least-mean squares (LMS) family of algorithms, in which we use deconvolution as a means of separating the impulse-response from the system input data. The technique can be used as a substitute for ordinary LMS but it has the added advantages that can be used for constant input data (i.e. data which are not persistently exciting) and the stability limit is far simpler to calculate. Furthermore, the convergence properties are much faster than LMS under certain types of noise input.