DocumentCode :
695277
Title :
Adaptive step size kernel least mean square algorithm for Lorenz time series prediction
Author :
Shoaib, Bilal ; Qureshi, Ijaz Mansoor ; Butt, Sharjeel Abid ; Khan, Shafqat Ullah ; Khan, Wasim
Author_Institution :
Dept. of Electron. Eng., Int. Islamic Univ., Islamabad, Pakistan
fYear :
2015
fDate :
13-17 Jan. 2015
Firstpage :
218
Lastpage :
221
Abstract :
An adaptive stepsize kernel least mean square(AKLMS) algorithm is presented in this paper. A mechanism is introduced here to adjust the stepsize parameter in the output of the KLMS algorithm using gradient descent method. The proposed method improves the results by reducing the training time of the algorithm that also helps in converging to global minima instead of local minima. We named the algorithm, the adaptive stepsize KLMS algorithm. Simulation results for the prediction of chaotic Lorenz series is presented in the terms of mean square error as the figure of merit. To validate the algorithm, comparison is made with KLMS.
Keywords :
chaos; convergence; gradient methods; least mean squares methods; time series; AKLMS algorithm; Lorenz time series prediction; adaptive step size kernel least mean square algorithm; adaptive stepsize KLMS algorithm; algorithm training time; chaotic Lorenz series prediction; convergence; global minima; gradient descent method; local minima; mean square error; step size parameter adjustment; Adaptive filters; Kernel; Kernel LMS; LMS; Lorenz Chaotic Time Series;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applied Sciences and Technology (IBCAST), 2015 12th International Bhurban Conference on
Conference_Location :
Islamabad
Type :
conf
DOI :
10.1109/IBCAST.2015.7058507
Filename :
7058507
Link To Document :
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