Name
Abstract | ||
|---|---|---|
The practical environment settings are always dynamic and unpredictable. And the adaptive solution is deteriorated seriously unless we can accurately determine when the filter's adaptation actually converges. In this paper, based on the principle of orthogonality, a scheme is proposed for sharply judging the iteration's convergence to obtain the optimal estimation in practically unknown stationary or nonstationary circumstances. The discriminant obtained through the estimated mean-square values of the desired, output and error signals at each iteration cycle, can be updated according to the varying characteristics of the actual input signal. Cases of both computer simulations and real applications are studied to validate its effectiveness in stationary and nonstationary environments |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICARCV.2006.345138 | ICARCV |
Keywords | Field | DocType |
mean-square value estimation,orthogonality principle,lms adaptive algorithm,approximation theory,optimal identification & estimation,parameter estimation,adaptive algorithm,iteration convergence,convergence time,optimal identification,estimation theory,least mean squares methods,optimal estimation,convergence discrimination on-line,orthogonality,practical noisy environments,least square method,iterative methods,computer simulation | Convergence (routing),Iterative method,Computer science,Control theory,Approximation theory,Orthogonality,Optimal estimation,Adaptive algorithm,Estimation theory,Orthogonality principle | Conference |
ISSN | ISBN | Citations |
2474-2953 | 1-4214-042-1 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huijuan Wu | 1 | 33 | 4.41 |
| Ping Li | 2 | 12 | 7.33 |
| Yumei Wen | 3 | 11 | 7.32 |