Please use this identifier to cite or link to this item: http://localhost:80/xmlui/handle/123456789/1972
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dc.contributor.authorShoaib, Bilal-
dc.contributor.authorQureshi, Ijaz Mansoor-
dc.contributor.authorShafqatullah-
dc.contributor.authorIhsanulhaq-
dc.date.accessioned2019-12-09T09:43:06Z-
dc.date.available2019-12-09T09:43:06Z-
dc.date.issued2014-01-01-
dc.identifier.issn23 050503-
dc.identifier.urihttp://142.54.178.187:9060/xmlui/handle/123456789/1972-
dc.description.abstractThis paper presents an adaptive step-size modified fractional least mean square (AMFLMS) algorithm to deal with a nonlinear time series prediction. Here we incorporate adaptive gain parameters in the weight adaptation equation of the original MFLMS algorithm and also introduce a mechanism to adjust the order of the fractional derivative adaptively through a gradient-based approach. This approach permits an interesting achievement towards the performance of the filter in terms of handling nonlinear problems and it achieves less computational burden by avoiding the manual selection of adjustable parameters. We call this new algorithm the AMFLMS algorithm. The predictive performance for the nonlinear chaotic Mackey Glass and Lorenz time series was observed and evaluated using the classical LMS, Kernel LMS, MFLMS, and the AMFLMS filters. The simulation results for the Mackey glass time series, both without and with noise, confirm an improvement in terms of mean square error for the proposed algorithm. Its performance is also validated through the prediction of complex Lorenz series.en_US
dc.language.isoen_USen_US
dc.publisherChinese Physics Ben_US
dc.subjectNatural Scienceen_US
dc.subjectmodified fractionalen_US
dc.subjectleast mean square algorithmen_US
dc.subjectchaotic timeen_US
dc.subjectseries predictionen_US
dc.titleAdaptive step-size modified fractional least mean square algorithm for chaotic time series predictionen_US
dc.typeArticleen_US
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