Research Article
Peformance Analysis of Mixture Approaches and Tracking Performance of Adaptive Filter using Adaptive Neural Network
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 82 - Issue 6 |
| Published: November 2013 |
| Authors: A. Vijayalakshmi, D. Spoorthi |
10.5120/14121-2227
|
A. Vijayalakshmi, D. Spoorthi . Peformance Analysis of Mixture Approaches and Tracking Performance of Adaptive Filter using Adaptive Neural Network. International Journal of Computer Applications. 82, 6 (November 2013), 27-33. DOI=10.5120/14121-2227
@article{ 10.5120/14121-2227,
author = { A. Vijayalakshmi,D. Spoorthi },
title = { Peformance Analysis of Mixture Approaches and Tracking Performance of Adaptive Filter using Adaptive Neural Network },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 82 },
number = { 6 },
pages = { 27-33 },
doi = { 10.5120/14121-2227 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A A. Vijayalakshmi
%A D. Spoorthi
%T Peformance Analysis of Mixture Approaches and Tracking Performance of Adaptive Filter using Adaptive Neural Network%T
%J International Journal of Computer Applications
%V 82
%N 6
%P 27-33
%R 10.5120/14121-2227
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper mainly concentrates on different mixture structures which include affine and convex combinations of several parallel running adaptive filters. The mixture structures are investigated using their final MSE values and the tracking of the nonlinear system is done using an ANN model that updates the filter weights using nonlinear learning strategies(it uses stochastic gradient descent to update the filter weights based on MSE's of mixture structures). the mixture structures greatly improve the convergence and performance of the of the constituent filters compared to traditional adaptive methods. The mixture structures employed in this paper can be applied to the constituent filters that employ different adaptation algorithms. We describe an adaptive neural network model that updates the weights of the filter using nonlinear methods.
References
- Suleyman Serdar Kozat, Member, IEEE, Alper Tunga Erdogan, Member, IEEE, Andrew C. Singer, Fellow, IEEE, and Ali H. Sayed, Fellow, IEEE,"steday state MSE performance analysis of mixture approaches to adaptive filtering",IEEETRANSACTIONS ON SIGNALPROCESSING VOL. ,58,AUGUST 2010
- A. C. Singer and M. Feder, "Universal linear prediction by model order weighting," IEEE Trans. Signal Process. , vol. 47, no. 10, pp. 2685–2699, Oct. 1999.
- J. Arenas-Garcia, A. R. Figueiras-Vidal, and A. H. Sayed, "Mean-square performance of a convex combination of two adaptive ?lters," IEEE Trans. Signal Process. , vol. 54, no. 3, pp. 1078–1090, Mar. 2006.
- Neil J. Bershad, Fellow, IEEE, José Carlos M. Bermudez, Senior Member, IEEE, and Jean-Yves Tourneret, Member, IEEEAn "Af?ne Combination of Two LMS Adaptive Filters—Transient Mean-Square Analysis"IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 56, NO. 5, MAY 2008
- E. Eweda, "Comparison of RLS, LMS and sign algorithms for tracking randomly time-varying channels," IEEE Trans. Signal Process. , vol. 42, no. 11, pp. 2937–2944, Nov. 1994.
- W. Zhuang, "RLS algorithm with variable forgetting factor for decision feedback equalizer over time-variant fading channels," Wireless Per- sonal Commun. , vol. 8, pp. 15–29, 1998.
- A. H. Sayed, Fundamentals of Adaptive Filtering. New York: Wiley, 2003.
- Dejan Kihas, ?Zeljko M. Djurovi´c, Branko D. Kova?cevi´ c"Adaptive Filtering based on Recurrent Neural Networks"JOURNAL OF AUTOMATIC CONTROL,UNIVERSITY OF BELGRADE,VOL. 13(1):13-24, 2003 c
- S. S. Kozat, A. C. Singer, and G. Zeitler, "Universal piecewise linearprediction via context trees," IEEE Trans. Signal Process. , vol. 55, no. 7, pp. 3730–3745, Jul. 2007.
- V. Vovk, "Competitive on-line statistics," Int. Stat. Rev. , vol. 69, pp. 213–248, 2001.
- N. Littlestone and M. K. Warmuth, "The weighted majority algorithm,"Inf. Comput. , vol. 108, no. 2, pp. 212–261, 1994.
- D. Haussler and J. Kivinen, "Additive versus exponentiated gradientupdates for linear prediction," J. Inf. Comput. , vol. 132, no. 1, pp. 1–64,1997.
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