Nonparametric Variable Step-Size LMAT Algorithm
- 292 Downloads
This paper proposes a nonparametric variable step-size least mean absolute third (NVSLMAT) algorithm to improve the capability of the adaptive filtering algorithm against the impulsive noise and other types of noise. The step-size of the NVSLMAT is obtained using the instantaneous value of a current error estimate and a posterior error estimate. This approach is different from the traditional method of nonparametric variance estimate. In the NVSLMAT algorithm, fewer parameters need to be set, thereby reducing the complexity considerably. Additionally, the mean of the additive noise does not necessarily equal zero in the proposed algorithm. In addition, the mean convergence and steady-state mean-square deviation of the NVSLMAT algorithm are derived and the computational complexity of NVSLMAT is analyzed theoretically. Furthermore, the experimental results in system identification applications presented illustrate the principle and efficiency of the NVSLMAT algorithm.
KeywordsLMAT Variable step-size Impulsive noise Nonparametric Most of the noise densities System identification
This work was partially supported by the National Natural Science Foundation of China (Grant: 61074120) and the Ph.D. Programs Foundation of the Ministry of Education of China (Grant: 20110203110004).
- 4.S.H. Cho, S.D. Kim, H.P. Moom, J.Y. NA, The least mean absolute third (LMAT) adaptive algorithm: mean and mean-squared convergence properties. In Proceedings of Sixth Western Pacific Reg. Acoust. Conf., Hong Kong, 22(10), 2303–2309 (1997)Google Scholar
- 8.X.Z. FU, Z. Liu, C.X. LI, Anti-interference performance improvement for sigmoid function variable step-size LMS adaptive algorithm. J. Beijing Univ. Posts Telecommun. 34(6), 112–120 (2011)Google Scholar
- 10.S.D. Kim, S.S. Kim, S.H. Cho, Least mean absolute third (LMAT) adaptive algorithm: part II. Perform. Eval. Algorithm 22(10), 2310–2316 (1997)Google Scholar
- 17.M.R. Spiegel, Mathematical Handbook of Formulas and Tables (McGraw-Hill, New York, 2012)Google Scholar
- 21.X. Yu, J.C. Liu, H.R. Li, An adaptive inertia weight particle swarm optimization algorithm for IIR digital filter. In Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI2009), pp. 114–118 (2009)Google Scholar