Name
Abstract | ||
|---|---|---|
This paper presents a new cost function for updating adaptive filter parameters. We utilize a nonlinear cost function that assigns a small step size for large errors and a large step size when the error is small, in order to get fast convergence and small steady state misadjustment. The cost function is derived by assuming that the error is t(λ) distributed. The convergence and the misadjustment mean square error of the proposed method adaptive system output are compared with those of the conventional least square algorithm. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/APCCAS.2002.1114978 | Circuits and Systems, 2002. APCCAS '02. 2002 Asia-Pacific Conference |
Keywords | DocType | Volume |
adaptive filters,convergence of numerical methods,error statistics,mean square error methods,LMS,adaptive filter parameter updating algorithms,adaptive system output misadjustment mean square error,convergence,error dependent nonlinear cost function step size,error distribution,least square algorithms,steady state misadjustment | Conference | 1 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junibakti Sanubari | 1 | 4 | 2.23 |
| Keiichi Tokuda | 2 | 3016 | 250.00 |