Naveed Ishtiaq Chaudhary

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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(More)
In this study, strength of evolutionary computational intelligence based on genetic algorithms (GAs) is exploited for parameter identification of nonlinear Hammerstein controlled autoregressive (NHCAR) systems. The fitness function is constructed for the NHCAR system by defining an error function in the mean square sense. Unknown adjustable weights of the(More)
In the present work, strength of momentum least mean square (MLMS) algorithm is exploited for nonlinear system identification problems represented with Hammerstein model. The MLMS algorithm uses the previous gradient information to estimate the current weights instead of using only current value of gradient; thus, it is faster in the convergence and less(More)
In the present study, a novel generalization of Volterra least mean square (V-LMS) algorithm to fractional order is presented by exploiting the renowned strength of fractional adaptive signal processing. The fractional derivative term is introduced in weight adaptation mechanism of standard V-LMS to derive the recursive relations for modified V-LMS (MV-LMS)(More)
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