Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks

1994 ◽  
Vol 5 (2) ◽  
pp. 279-297 ◽  
Author(s):  
G.V. Puskorius ◽  
L.A. Feldkamp
Keyword(s):  
Kalman Filter ◽  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Recurrent Networks ◽  
Nonlinear Dynamical

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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