FIR System Identification Using Feedback

T. J. Moir

School of Engineering, AUT University, Auckland, New Zealand.

DOI: 10.4236/jsip.2013.44049                

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.

Keywords

LMS; Feedback; System Identification

Conflicts of Interest

The authors declare no conflicts of interest.

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