Name
Abstract | ||
|---|---|---|
The recursive least square (RLS) and the normalized least mean square (NLMS) algorithms are proposed for canonical piecewise linear (PWL) adaptive filters. The parameters are updated recursively in a manner similar to back-propagation. The simulation results indicate PWL adaptive filters can suitably model nonlinear systems |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/78.575711 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
pwl nonlinear adaptive filter,model nonlinear system,adaptive filter,mean square,nlms algorithm,simulation result,pwl adaptive filter,least squares approximation,back propagation,vectors,adaptive signal processing,rls algorithm,neural networks,adaptive filters,piecewise linear,nonlinear system | Least squares,Least mean squares filter,Signal processing,Mathematical optimization,Nonlinear system,Computer simulation,Control theory,Algorithm,Adaptive filter,Piecewise linear function,Mathematics,Recursive least squares filter | Journal |
Volume | Issue | ISSN |
45 | 5 | 1053-587X |
Citations | PageRank | References |
2 | 0.45 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Plaziac | 1 | 29 | 2.61 |
| Chon Ledinh | 2 | 2 | 0.45 |
| J.-P. Adoul | 3 | 21 | 3.34 |