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

查看全文 作　　者：[1]BilalShoaib;Ijaz Mansoor [2]Qureshi;[3]Shafqatullah;[1]Ihsanulhaq

机构地区：[1]Department of Electronic Engineering, Faculty of Engineering and Technology, International Islamic University Islamabad, Pakistan;[2]Department of Electrical Engineering, AIR University, Islamabad, Pakistan;[3]School of Engineering and Applied Sciences, ISRA University, Islamabad, Pakistan

出　　处：《Chinese Physics B》索引2014年第23卷第5期,共9页

基　　金：Project supported by the Higher Education Commission of Pakistan 摘　　要：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. 关键词：混沌时间序列预测最小均方算法自适应步长分数阶导数修改 FLMS算法非线性时间序列预测权重调整

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

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