Nonlinear state-space modeling using recurrent multilayer perceptrons with unscented Kalman filter

2005 ◽  
Author(s):  
Jongsoo Choi ◽  
Tet Hin Yeap ◽  
M. Bouchard
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Unscented Kalman Filter ◽  
Multilayer Perceptrons ◽  
Space Modeling ◽  
State Space Modeling ◽  
Nonlinear State

Monitoring pupil development by means of the Kalman filter and smoother based upon sem state space modeling

1998 ◽  
Vol 10 (2) ◽  
pp. 103-119 ◽  
Author(s):  
Johan H.L. Oud ◽  
Robert A.R.G. Jansen ◽  
Jan F.J. van Leeuwe ◽  
Cor A.J. Aarnoutse ◽  
Marinus J.M. Voeten
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Space Modeling ◽  
State Space Modeling

Bayesian state-space modeling of age-structured data: fitting a model is just the beginning

2000 ◽  
Vol 57 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Russell B Millar ◽  
Renate Meyer
Keyword(s):  
State Space ◽  
Research Effort ◽  
Population Analysis ◽  
Structured Data ◽  
Simple Task ◽  
Implementation Phase ◽  
Space Modeling ◽  
State Space Modeling ◽  
Age Structured ◽  
Nonlinear State

Explicit modeling of process variability in the dynamics of fisheries is motivated by a desire to incorporate more realism into stock assessment models, and much recent research effort has been devoted to the computational features of fitting state-space models for this purpose. Here, we extend the Bayesian application of nonlinear state-space modeling to sequential population analysis of age-structured data using a model formulation that allows for unreported catches and incidental fishing mortality. It is shown that, once a familiarity with the general-purpose Bayesian software BUGS is acquired, implementing a state-space model is a relatively simple task. Indeed, this application requires just 18 lines of code in its entirety and does not require the programmer to know the formulae for any prior density functions or likelihoods. Consequently, we suggest that this methodology may permit the implementation phase of nonlinear state-space modeling to be relegated, thereby allowing more effort to be devoted to the challenging issues of model checking, selection/averaging, sensitivity, and prior specification.

scholarly journals Improved Initialization for Nonlinear State-Space Modeling

2014 ◽  
Vol 63 (4) ◽  
pp. 972-980 ◽  
Author(s):  
Anna Marconato ◽  
Jonas Sjoberg ◽  
Johan A. K. Suykens ◽  
Johan Schoukens
Keyword(s):  
State Space ◽  
Space Modeling ◽  
State Space Modeling ◽  
Nonlinear State
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