Name
Abstract | ||
|---|---|---|
A large class of non-linear phenomena can be described using bilinear systems. Such systems are very attractive since they usually require few parameters, to approximate most non-linearities (compared to other systems). This paper addresses the problems of bilinear system identification using Bayesian inference. The Gibbs sampler is used to estimate the bilinear system parameters, from measurements of the system input and output signals. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/ICASSP.1998.681761 | PROCEEDINGS OF THE 1998 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-6 |
Keywords | Field | DocType |
kernel,seismology,markov processes,signal processing,bayesian methods,polynomials,identification,gibbs sampler,feedback,bayesian inference,nonlinear systems,parameter estimation,monte carlo methods | Mathematical optimization,Monte Carlo method,Markov process,Nonlinear phenomena,Bayesian inference,Computer science,Bilinear systems,Input/output,Estimation theory,Gibbs sampling | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.47 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Souad Meddeb | 1 | 3 | 1.57 |
| Jean-Yves Tourneret | 2 | 835 | 64.32 |
| F. Castanie | 3 | 116 | 16.29 |