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A modified fractional least mean square algorithm for chaotic and nonstationary time series prediction

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dc.contributor.author Shoaib, Bilal
dc.contributor.author Qureshi, Ijaz Mansoor
dc.contributor.author Ihsanulhaq
dc.contributor.author Shafqatullah
dc.date.accessioned 2019-11-29T04:59:39Z
dc.date.available 2019-11-29T04:59:39Z
dc.date.issued 2014-01-01
dc.identifier.issn 23 030502
dc.identifier.uri http://142.54.178.187:9060/xmlui/handle/123456789/1839
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher Institue of physics en_US
dc.subject Natural Science en_US
dc.subject least mean square en_US
dc.subject algorithm en_US
dc.subject chaotic en_US
dc.subject nonstationary time en_US
dc.subject series prediction en_US
dc.title A modified fractional least mean square algorithm for chaotic and nonstationary time series prediction en_US
dc.type Article en_US


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