Title :
Multiple model estimation represented by Bayesian networks
Author :
Yan, Liang ; Donghua, Zhou ; Quan, Pan
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Abstract :
Multiple Model Estimation (MME) in hybrid systems, as a powerful approach to adaptive estimation, has been widely applied in a great deal of attention due to its unique power to handle problems with both structural and parametric uncertainties. In this paper, multiple well-known methods in MME are represented in the form of Bayesian Networks (BN), which is widely used in artificial intelligence. The discussion implies that MME may be a special case of BN.
Keywords :
Markov processes; adaptive estimation; belief networks; uncertainty handling; Bayesian networks; Markov transition probability; adaptive estimation; artificial intelligence; hybrid systems; multiple model estimation; one-step memory methods; parametric uncertainties; structural uncertainties; two-step memory methods; Adaptive estimation; Artificial intelligence; Automation; Bayesian methods; Fault detection; Intelligent networks; Intelligent robots; Power system modeling; Target tracking; Uncertainty;
Conference_Titel :
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN :
0-7803-7268-9
DOI :
10.1109/WCICA.2002.1020696
