The modified gain extended Kalman filter and parameter identification in linear systems

Automatica ◽  
1986 ◽  
Vol 22 (1) ◽  
pp. 59-75 ◽  
Author(s):  
Taek L. Song ◽  
Jason L. Speyer
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Linear Systems ◽  
Extended Kalman Filter

Dynamic Modeling and Parameter Identification for Biological Networks

2011 ◽  
pp. 478-510 ◽  
Author(s):  
Fortunato Bianconi ◽  
Gabriele Lillacci ◽  
Paolo Valigi
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Dynamic Modeling ◽  
Biological Networks ◽  
In Silico ◽  
High Gain ◽  
The Other ◽  
Sensitivity Analyses ◽  
Identification Techniques

Then, two different parameter identification techniques are presented for the proposed models. One is based on a least squares procedure, which treats the signals provided by a high gain observer; the other one is based on a Mixed Extended Kalman Filter. Prior to the estimation phase, identifiability and sensitivity analyses are used to determine which parameters can be and/or should be estimated. The procedures are tested and compared by means of data obtained by in silico experiments.

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