Please use this identifier to cite or link to this item: http://localhost:80/xmlui/handle/123456789/1839
Title: A modified fractional least mean square algorithm for chaotic and nonstationary time series prediction
Authors: Shoaib, Bilal
Qureshi, Ijaz Mansoor
Ihsanulhaq
Shafqatullah
Keywords: Natural Science
least mean square
algorithm
chaotic
nonstationary time
series prediction
Issue Date: 1-Jan-2014
Publisher: Institue of physics
Abstract: A method of modifying the architecture of fractional least mean square (FLMS) algorithm is presented to work with nonlinear time series prediction. Here we incorporate an adjustable gain parameter in the weight adaptation equation of the original FLMS algorithm and absorb the gamma function in the fractional step size parameter. This approach provides an interesting achievement in the performance of the filter in terms of handling the nonlinear problems with less computational burden by avoiding the evaluation of complex gamma function. We call this new algorithm as the modified fractional least mean square (MFLMS) algorithm. The predictive performance for the nonlinear Mackey glass chaotic time series is observed and evaluated using the classical LMS, FLMS, kernel LMS, and proposed MFLMS adaptive filters. The simulation results for the time series with and without noise confirm the superiority and improvement in the prediction capability of the proposed MFLMS predictor over its counterparts.
URI: http://142.54.178.187:9060/xmlui/handle/123456789/1839
ISSN: 23 030502
Appears in Collections:Journals

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