Parameter estimation and control for a neural mass model based on the unscented Kalman filter

2013 ◽  
Vol 88 (4) ◽  
Author(s):  
Xian Liu ◽  
Qing Gao
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Neural Mass Model ◽  
Mass Model ◽  
Model Based ◽  
Neural Mass ◽  
Estimation And Control ◽  
And Control

scholarly journals Neural mass model-based tracking of anesthetic brain states

NeuroImage ◽  
2016 ◽  
Vol 133 ◽  
pp. 438-456 ◽  
Author(s):  
Levin Kuhlmann ◽  
Dean R. Freestone ◽  
Jonathan H. Manton ◽  
Bjorn Heyse ◽  
Hugo E.M. Vereecke ◽  
...  
Keyword(s):  
Neural Mass Model ◽  
Mass Model ◽  
Model Based ◽  
Neural Mass ◽  
Brain States

scholarly journals Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Roberta Veloso Garcia ◽  
Helio Koiti Kuga ◽  
Maria Cecilia F. P. S. Zanardi
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Attitude Determination ◽  
Unscented Kalman Filter ◽  
Artificial Satellite ◽  
Real Data ◽  
Nonlinear Functions ◽  
Estimation And Control ◽  
And Control

The aim of this work is to test an algorithm to estimate, in real time, the attitude of an artificial satellite using real data supplied by attitude sensors that are on board of the CBERS-2 satellite (China Brazil Earth Resources Satellite). The real-time estimator used in this work for attitude determination is the Unscented Kalman Filter. This filter is a new alternative to the extended Kalman filter usually applied to the estimation and control problems of attitude and orbit. This algorithm is capable of carrying out estimation of the states of nonlinear systems, without the necessity of linearization of the nonlinear functions present in the model. This estimation is possible due to a transformation that generates a set of vectors that, suffering a nonlinear transformation, preserves the same mean and covariance of the random variables before the transformation. The performance will be evaluated and analyzed through the comparison between the Unscented Kalman filter and the extended Kalman filter results, by using real onboard data.

A neural mass model based on single cell dynamics to model pathophysiology

2014 ◽  
Vol 37 (3) ◽  
pp. 549-568 ◽  
Author(s):  
Bas-Jan Zandt ◽  
Sid Visser ◽  
Michel J. A. M. van Putten ◽  
Bennie ten Haken
Keyword(s):  
Single Cell ◽  
Neural Mass Model ◽  
Mass Model ◽  
Cell Dynamics ◽  
Model Based ◽  
Neural Mass
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