Browsing by Author "Khan, Zeshan Aslam"
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- Design of auxiliary model based normalized fractional gradient algorithm for nonlinear output-error systemsPublication . Chaudhary, Naveed Ishtiaq; Khan, Zeshan Aslam; Kiani, Adiqa Kausar; Raja, Muhammad Asif Zahoor; Chaudhary, Iqra Ishtiaq; Pinto, Carla M. A.A new avenue of fractional calculus applications has emerged that investigates the design of fractional gradient based novel iterative methods for analyzing fractals and nonlinear dynamics in solving engineering and applied sciences problems. The most discussed algorithm in this regard is fractional least mean square (FLMS) algorithm. This study presents an auxiliary model based normalized variable initial value FLMS (AM-NVIV-FLMS) algorithm for input nonlinear output error (INOE) system identification. First, NVIV-FLMS is presented to automatically tune the learning rate parameter of VIV-FLMS and then the AM-NVIV-FLMS is introduced by incorporating the auxiliary model idea that replaces the unknown values of the information vector with the output of auxiliary model. The proposed AM-NVIV-FLMS scheme is accurate, convergent, robust and reliable for INOE system identification. Simulation results validate the significance and efficacy of the proposed scheme.
- Design of multi innovation fractional LMS algorithm for parameter estimation of input nonlinear control autoregressive systemsPublication . Chaudhary, Naveed Ishtiaq; Raja, Muhammad Asif Zahoor; He, Yigang; Khan, Zeshan Aslam; Machado, J. A. TenreiroThe development of procedures based on fractional calculus is an emerging research area. This paper presents a new perspective regarding the fractional least mean square (FLMS) adaptive algorithm, called multi innovation FLMS (MIFLMS). We verify that the iterative parameter adaptation mechanism of the FLMS uses merely the current error value (scalar innovation). The MIFLMS expands the scalar innovation into a vector innovation (error vector) by considering data over a fixed window at each iteration. Therefore, the MIFLMS yields better convergence speed than the standard FLMS by increasing the length of innovation vector. The superior performance of the MIFLMS is verified through parameter identification problem of input nonlinear systems. The statistical performance indices based on multiple independent trials confirm the consistent accuracy and reliability of the proposed scheme.