A micropower analog circuit implementation of hidden Markov model state decoding

1997 ◽  
Vol 32 (8) ◽  
pp. 1200-1209 ◽  
Author(s):  
J. Lazzaro ◽  
J. Wawrzynek ◽  
R.P. Lippmann
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Analog Circuit ◽  
Hidden Markov ◽  
Circuit Implementation ◽  
Model State ◽  
Analog Circuit Implementation

scholarly journals Selecting hidden Markov model state number with cross-validated likelihood

2007 ◽  
Vol 23 (4) ◽  
pp. 541-564 ◽  
Author(s):  
Gilles Celeux ◽  
Jean-Baptiste Durand
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Model State

scholarly journals Trajectory learning using posterior hidden Markov model state distribution

2017 ◽  
Vol 18 (3) ◽  
pp. 171-180 ◽  
Author(s):  
Asmaa A.E. Osman ◽  
Reda A. El-Khoribi ◽  
Mahmoud E. Shoman ◽  
M.A. Wahby Shalaby
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
State Distribution ◽  
Model State ◽  
Trajectory Learning

Continuous Hidden Markov Model Based Incipient Fault Monitoring in Filtered Analog Circuits

2013 ◽  
Vol 347-350 ◽  
pp. 181-186
Author(s):  
Yong Cai Ao ◽  
Yi Bing Shi ◽  
Wei Zhang ◽  
Yan Jun Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Hidden Markov ◽  
Small Deviation ◽  
Soft Fault ◽  
Nominal Parameter ◽  
Incipient Fault ◽  
Hidden States

The small variations of components parameters often lead to severe performance degradation in Filtered Analog Circuits (FAC). Most of the researches on soft fault diagnosis in analog circuit are focused on the variations beyond 30% of the nominal parameter in recent years, which is usually unacceptable in FAC. To diagnose the soft fault of small deviation as earlier as possible, the Hidden Markov Model (HMM) was introduced to monitor the FAC. Different from the existing diagnosis approaches based on HMM, in which the variations of components parameters were considered to be random, the continuous variations of the fault component parameter are discretized and modeled by the hidden states of the proposed HMM method. The experiment demonstrates that the proposed HMM approach can model the FAC effectively and recognize the incipient fault earlier.

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