title: Identification of input nonlinear control autoregressive systems using fractional signal processing approach.
abstract: A novel algorithm is developed based on fractional signal processing approach for parameter estimation of input nonlinear control autoregressive (INCAR) models. The design scheme consists of parameterization of INCAR systems to obtain linear-in-parameter models and to use fractional least mean square algorithm (FLMS) for adaptation of unknown parameter vectors. The performance analyses of the proposed scheme are carried out with third-order Volterra least mean square (VLMS) and kernel least mean square (KLMS) algorithms based on convergence to the true values of INCAR systems. It is found that the proposed FLMS algorithm provides most accurate and convergent results than those of VLMS and KLMS under different scenarios and by taking the low-to-high signal-to-noise ratio.
authors.affiliation: Department of Electronic Engineering, International Islamic University, Islamabad 44000, Pakistan.
authors.forename: Naveed Ishtiaq
authors.initials: NI
authors.lastname: Chaudhary
authors.forename: Muhammad Asif Zahoor
authors.initials: MA
authors.lastname: Raja
authors.forename: Junaid Ali
authors.initials: JA
authors.lastname: Khan
authors.forename: Muhammad Saeed
authors.initials: MS
authors.lastname: Aslam
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted