Identification of Input Nonlinear Control Autoregressive Systems Using Fractional Signal Processing Approach

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 squar...

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Bibliographic Details
Published in:TheScientificWorld 2013-01, Vol.2013 (2013), p.1-13
Main Authors: Aslam, Muhammad Saeed, Khan, Junaid Ali, Raja, Muhammad Asif Zahoor, Chaudhary, Naveed Ishtiaq
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Autoregressive models
Computer Simulation
Convergence
Data processing
Design parameters
Engineering
Feedback
Identification
International conferences
Mathematical research
Mean square values
Models, Statistical
Noise control
Nonlinear control
Nonlinear Dynamics
Nonlinear systems
Nonlinear theories
Parameter estimation
Parameterization
Regression Analysis
Signal processing
Signal Processing, Computer-Assisted
Speech
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Summary: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.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2013/467276