Title :
On-line Bayesian change detection scheme for unknown nonlinear systems via sequential Monte Carlo
Author :
Nakada, Yohei ; Matsumoto, Takashi
Author_Institution :
Graduate Sch. of Sci. & Eng., Waseda Univ., Tokyo, Japan
fDate :
July 31 2005-Aug. 4 2005
Abstract :
An attempt is made to perform on-line change detection given sequential data from an unknown nonlinear system. The algorithm sequentially estimates the probability of occurrence of a change within a Bayesian framework. The implementation is done via sequential Monte Carlo (SMC). The proposed scheme is tested against two specific examples.
Keywords :
Bayes methods; Monte Carlo methods; neural nets; nonlinear systems; sequential estimation; online Bayesian change detection; sequential Monte Carlo; sequential data; sequential estimation; unknown nonlinear system; Bayesian methods; Change detection algorithms; Data analysis; Monte Carlo methods; Multilayer perceptrons; Nonlinear systems; Signal processing algorithms; Sliding mode control; Uncertainty; Yttrium;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556243
