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Fault Diagnosis Based on Likelihood Decomposition
Katsuji Uosaki,Tetsuo Kagawa 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
A novel fault diagnosis method based on likelihood decomposition is proposed for linear stochastic systems described by autoregressive (AR) model. Assuming that at some time instant γ the fault of one of the following two types is occurs: innovation fault (actuator fault); and observation fault (sensor fault), the log-likelihood function is decomposed into two components based on the observations before and after γ, respectively, Then, the type of the fault is determined by comparing the log-likelihoods corresponding two types of faults. Numerical examples demonstrate the usefulness of the proposed diagnosis method.
Applying Particle Filter to Genetic Regulatory Networks Identification
Kotaro Miake,Toshiharu Hatanaka,Katsuji Uosaki 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
The genetic regulatory networks(GRNs) identification problem is considered in this paper. Since it prefers to capture the characteristic behavior of GRN, which seems natural to describe as a hybrid system, a piecewise affine type model is often used as a simple model of GRN sinrecent years. A particle filter is introduced to identify a piece wise ARX model, where the system mode and parameters should be estimated simultaneously. The system mode is estimated by maximum a posteriori probability and the unknown parameters are estimated by particles. A numerical simulation studies are carried out by using the carbon starvation response model of the bacterium Escherichia coli, and the proposed method is able to estimate the system mode with high accuracy.