Title :
Identification of bilinear systems using Bayesian inference
Author :
Meddeb, Souad ; Tourneret, Jean Yves ; Castanie, Francis
Author_Institution :
ENSEEIHT/GAPSE, Toulouse, France
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
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; bilinear systems; discrete time systems; inference mechanisms; parameter estimation; signal sampling; Bayesian inference; Gibbs sampler; bilinear systems; identicalness; identification; nonlinear phenomena; nonlinearities; system input; system output; Bayesian methods; Earthquakes; Explosions; Feedback; Kernel; Nonlinear systems; Parameter estimation; Polynomials; Seismology; Signal processing;
Conference_Titel :
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-4428-6
DOI :
10.1109/ICASSP.1998.681761
