Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems

In the present study, single and two-stage least mean square (LMS) adaptive strategies based on fractional signal processing are developed for parameter estimation of controlled autoregressive moving average (CARMA) systems. The main idea is to use fractional LMS identification (FLMSI) and two-stage FLMSI (TS-FLMSI) algorithms for CARMA model that is decomposed into a system and noise models. The performance analyses for both proposed FLMSI and TS-FLMSI schemes are conducted based on adapting the prior known design parameters of the system and comparing the results with standard adaptive algorithms. The accuracy and convergence of the design schemes are verified and validated through the results of statistical analyses based on sufficient number of independent runs to adapt CARMA system. Comparative studies established the dominance of single and two-stage fractional adaptive algorithms over other counterpart in term of model accuracy and reliability in case of different scenarios based on variant signal to noise ratios and step size parameters. (C) 2014 Elsevier B.V. All rights reserved.