scholarly journals Characterizing Fluctuations of Arterial and Cerebral Tissue Oxygenation in Preterm Neonates by Means of Data Analysis Techniques for Nonlinear Dynamical Systems

2016 ◽  
pp. 511-519 ◽  
Author(s):  
Stefan Kleiser ◽  
Marcin Pastewski ◽  
Tharindi Hapuarachchi ◽  
Cornelia Hagmann ◽  
Jean-Claude Fauchère ◽  
...  
Keyword(s):  
Data Analysis ◽  
Dynamical Systems ◽  
Tissue Oxygenation ◽  
Nonlinear Dynamical Systems ◽  
Preterm Neonates ◽  
Cerebral Tissue ◽  
Nonlinear Dynamical ◽  
Analysis Techniques ◽  
Cerebral Tissue Oxygenation

THE UNSCENTED KALMAN FILTER, A POWERFUL TOOL FOR DATA ANALYSIS

2004 ◽  
Vol 14 (06) ◽  
pp. 2093-2105 ◽  
Author(s):  
A. SITZ ◽  
U. SCHWARZ ◽  
J. KURTHS
Keyword(s):  
Kalman Filter ◽  
Data Analysis ◽  
Dynamical Systems ◽  
Unscented Kalman Filter ◽  
Nonlinear Dynamical Systems ◽  
Simultaneous Estimation ◽  
Bayesian Filter ◽  
Nonlinear Dynamical

We present a derivation of the unscented Kalman filter (UKF) as an approximation to the optimal Bayesian filter equations. The potentials of the UKF are then demonstrated for the problem of simultaneous estimation of states and parameters from noise corrupted data of nonlinear dynamical systems. The UKF even works for the chaotic Chua system which includes nondifferentiable terms.

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