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Adaptive step-size modified fractional least mean square algorithm for chaotic time series prediction

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dc.contributor.author Shoaib, Bilal
dc.contributor.author Qureshi, Ijaz Mansoor
dc.contributor.author Shafqatullah
dc.contributor.author Ihsanulhaq
dc.date.accessioned 2019-12-09T09:43:06Z
dc.date.available 2019-12-09T09:43:06Z
dc.date.issued 2014-01-01
dc.identifier.issn 23 050503
dc.identifier.uri http://142.54.178.187:9060/xmlui/handle/123456789/1972
dc.description.abstract This 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.iso en_US en_US
dc.publisher Chinese Physics B en_US
dc.subject Natural Science en_US
dc.subject modified fractional en_US
dc.subject least mean square algorithm en_US
dc.subject chaotic time en_US
dc.subject series prediction en_US
dc.title Adaptive step-size modified fractional least mean square algorithm for chaotic time series prediction en_US
dc.type Article en_US


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