Malaysian Journal of Mathematical Sciences, March 2016, Vol. 10(S)
Special Issue: The 10th IMT-GT International Conference on Mathematics, Statistics and its Applications 2014 (ICMSA 2014)
Kernel Based Learning for Nonlinear System Identification
Shazia Javed and Noor Atinah Ahmad
Corresponding Email: shaziafateh@hotmail.com
Received date: -
Accepted date: -
Abstract:
In this paper, an efficient Kernel based algorithm is developed with application in nonlinear system identification. Kernel adaptive filters are famous for their universal approximation property with Gaussian kernel, and online learning capabilities. The proposed adaptive step-size KLMS (ASS-KLMS) algorithm can exhibit universal approximation capability, irrespective of the choice of reproducing kernel. Performance evaluation of proposed nonlinear adaptive filter is carried out for an unknown plant with an additive white Gaussian noise in the expected output. In comparative study, learning parameters are computed using KLMS, NKLMS and ASS-KLMS algorithms for polynomial and Gaussian kernels. Simulation results are presented for the performance analysis of the algorithm in terms of signal to noise ratio (SNR) and MSE [dB], showing preference of ASS-KLMS algorithm over the rest.
Keywords: On-line learning, Kernel filter, adaptive step-size, system modeling
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