Abstract
In this paper Normalized Kernel Least Mean Square (NKLMS) algorithm is presented which has applications in system modeling and pattern recognition. In 2007 a similar algorithm was proposed Named Kernel Least Mean Square (KLMS), and a modified version of KLMS was introduced in 2008. Although KLMS has good results in prediction of some time series, high sensitivity to step-size and signal amplitude stability, still remain as problems. In this paper NKLMS and its ability in prediction and identification of time series is presented and is compared to KLMS method. A variable named step-size that was used in the algorithm has made NKLMS more efficient in prediction of time-series which have inconsistency in amplitude. Thus, convergence speed and system tracking are improved. Furthermore the proposed algorithm is applied to channel modeling.
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