Model description:
The time-invariant bilinear system is given by
$$Y(t) = 1.5X(t) + 1.2X(t-1) - 0.2X(t-2) + 0.7X(t-1)Y(t-1) - 0.1X(t-2)Y(t-2) + \epsilon(t),$$
where $A=0, \alpha=0, B=\begin{bmatrix}1.5 &1.2 &-0.2\end{bmatrix}, C = \begin{bmatrix}0.7 &0 &-0.1\end{bmatrix}$. Note that $\Theta = \begin{bmatrix}B & C\end{bmatrix}^{\mathrm T}.$
Type:
Form:
Model order:
2
Time domain:
Linearity:
Publication details:
| Title | Identification of bilinear systems using Bayesian inference |
| Publication Type | Conference Paper |
| Year of Publication | 1998 |
| Authors | Meddeb, S., Tourneret J.Y., and Castanie F. |
| Conference Name | Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, 1998. |
| Date Published | 05/1998 |
| Publisher | IEEE |
| Conference Location | Seattle, WA |
| ISBN Number | 0-7803-4428-6 |
| Accession Number | 6053933 |
| Keywords | Bayes methods, bilinear systems, discrete time systems, inference mechanisms, Markov processes, Monte Carlo methods, parameter estimation, signal sampling |
| Abstract | A large class of nonlinear phenomena can be described using bilinear systems. Such systems are very attractive since they usually require few parameters, to approximate most nonlinearities (compared to other systems). This paper addresses the problems of bilinear system identicalness using Bayesian inference. The Gibbs sampler is used to estimate the bilinear system parameters, from measurements of the system input and output signals |
| DOI | 10.1109/ICASSP.1998.681761 |