Name
Abstract | ||
|---|---|---|
In this paper, we explore the use of a particular multistage adaptation algorithm for a variety of adaptive filtering applications where the structure of the underlying process to be estimated is unknown. The proposed algorithm uses a performance-weighted mixture of LMS filters of various orders to construct its final output. The algorithm is analyzed in a stochastic context with respect to its convergence and mean-square error (MSE) behaviors and is shown to achieve the best MSE performance of the constituent algorithms in the mixture. Through simulations, it has been observed that the mixture structure can offer considerable performance improvement for both stationary and time varying observation sequences. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICASSP.2009.4960280 | ICASSP |
Keywords | Field | DocType |
final output,mixture structure,performance-weighted mixture,lms filter,constituent algorithm,mse performance,mean-square error,proposed algorithm,particular multistage adaptation algorithm,considerable performance improvement,algorithm design and analysis,convergence,least squares approximation,adaptive filters,predictive models,prediction,universal,prediction algorithms,lattices,mean square error,adaptive filtering,adaptive filter,data mining | Convergence (routing),Least squares,Mathematical optimization,Algorithm design,Pattern recognition,Computer science,Prediction algorithms,Artificial intelligence,Adaptive filter,Signal processing algorithms,Performance improvement | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.38 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suleyman S. Kozat | 1 | 212 | 15.94 |
| Andrew C. Singer | 2 | 1224 | 104.92 |