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http://dx.doi.org/10.7840/kics.2015.40.9.1793

Kernel RLS Algorithm Using Variable Forgetting Factor  

Lim, Jun-Seok (Sejong University, Dept. of Electrical Engineering)
Pyeon, Yong-Guk (GangWon Provincial College, Dept. of Information and Communication)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.40, no.9, 2015 , pp. 1793-1801 More about this Journal
Abstract
In a recent work, kernel recursive least-squares tracker (KRLS-T) algorithm has been proposed. It is capable of tracking in non-stationary environments using a forgetting mechanism built on a Bayesian framework. The forgetting mechanism in KRLS-T is implemented by a fixed forgetting factor. In practice, however, we frequently meet that the fixed forgetting factor cannot handle time-varying system effectively. In this paper we propose a new KRLS-T with a variable forgetting factor. Experimental results show that proposed algorithm can handle time-varying system more effectively than the KRLS-T.
Keywords
RLS; KRLS; KRLS-T; variable forgetting factor;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 S. Haykin, Adaptive Filter Theory, 4th Ed., Prentice Hall, 2001.
2 A. H. Sayed, Fundamentals of Adaptive Filtering, Wiley-IEEE Press, 2003.
3 M. Choi and S. Lee, "Comparison study of channel estimation algorithm for 4S maritime communications," J. KICS, vol. 38C, no. 3, pp. 288-295, Mar. 2013.   DOI
4 J. S. Lim and Y. G. Pyeon, "Time delay estimation using LASSO (Least Absolute Selection and Shrinkage Operator)," J. KICS, vol. 39B, no. 10, pp. 715-721, Dec. 2014.   DOI
5 C. Hwang and K. Kim, "Doppler frequency estimation for time-varying underwater acoustic communication channel," J. KICS, vol. 40, no. 01, pp. 187-192, Jan. 2015.   DOI
6 Y. Engel, S. Mannor, and R. Meir, "The kernel recursive least squares algorithm," IEEE Trans. Signal Process., vol. 52, no. 8, pp. 2275-2285, Aug. 2004.   DOI   ScienceOn
7 W. Liu, J. C. Principe, and S. Haykin, Kernel Adaptive Filtering: A Comprehensive Introduction, Wiley, 2010.
8 V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag Inc., 1995.
9 B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 2001.
10 S. V. Vaerenbergh, J. Via, and I. Santamaria, "A sliding-window kernel RLS algorithm and its application to nonlinear channel identification," in Proc. IEEE ICASSP, pp. 789-792, Toulouse, France, May 2006.
11 M. Lazaro-Gredilla, S. V. Vaerenbergh, and I. Santamaria, "A Bayesian approach to tracking with kernel recursive least-squares," in Proc. IEEE Int. Wksp. MLSP, pp. 1-6, Beijing, China, Sept. 2011.
12 S. V. Vaerenbergh, M. Lazaro-Gredilla, and I. Santamaria, "Kernel recursive least-squares tracker for time varying regression," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 8, pp. 1313-1326, Aug. 2012.   DOI   ScienceOn
13 L. Csato and M. Opper, "Sparse online Gaussian processes," Neural Computation, vol. 14, no. 3, pp. 641-668, 2002.   DOI   ScienceOn
14 W. Liu, I. Park, and J. C. Principe, "An information theoretic approach of designing sparse kernel adaptive filters," IEEE Trans. Neural Netw., vol. 20, no. 12, pp. 1950-1961, Dec. 2009.   DOI   ScienceOn
15 S. Song, J. S. Lim, S. Baek, and K. M. Sung, "Gauss Newton variable forgetting factor recursive least squares for time varying parameter tracking," Electron. Lett., vol. 36, no. 11, pp. 988-990, Nov. 2000.   DOI   ScienceOn
16 S. Song, J. S. Lim, S. J. Baek, and K. M. Sung, "Variable forgetting factor linear least square algorithm for frequency selective fading channel estimation," IEEE Trans. Veh. Technol., vol. 51, no. 3, pp. 613-616, May 2002.   DOI   ScienceOn
17 J. S. Lim, S. Lee, and H. Pang, "Low complexity adaptive forgetting factor for online sequential extreme learning machine (OS-ELM) for application to nonstationary system estimations," Neural Comput. Appl., vol. 22, no. 3, pp. 569-576, May 2013.   DOI
18 S. Lee, J. S. Lim, and K. M. Sung, "Low-complexity VFF-RLS algorithm using normalization technique," J. Acoust. Soc. Kr., vol. 29, no. 1, pp. 18-23, Jan. 2010.
19 C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006.
20 L. Csato and M. Opper, Sparse representation for gaussian process models, in Neural Inf. Process. Syst. 13, pp. 444-450, Cambridge, MA: MIT Press, 2001.
21 A. K. Kohli and A. Rai, "Numeric variable forgetting factor RLS algorithm for second-order volterra filtering," Circuits Syst. Signal Process, vol. 32, no. 1, pp. 223-232, Feb. 2013.   DOI
22 S. V. Vaerenbergh, M. Lazaro-Gredilla, and I. Santamaria, "Kernel recursive least-squares tracker for time-varying regression," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 8, pp. 1313-1326, Aug. 2012.   DOI   ScienceOn
23 D. J. Park, B. E. Jun, and J. H. Kim, "Fast tracking RLS algorithm using novel variable forgetting factor with unity zone," Electron. Lett., vol. 27, no. 23, pp. 2150-2151, Nov. 1991.   DOI   ScienceOn