State space modelling of extreme values with particle filters [PDF]
, 2009 State space models are a flexible class of Bayesian model that can be used to smoothly capture non-stationarity. Observations are assumed independent given a latent state process so that their distribution can change gradually over time. Sequential Monte
Wyncoll, David P.
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Catalytic cracking models developed for predictive control purposes [PDF]
Modeling, Identification and Control, 1993 The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment.
Dag Ljungqvist +2 more
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Markov chain Monte Carlo methods for state-space models with point process observations [PDF]
, 2012 This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations.
Niranjan, Mahesan +2 more
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Combined Deterministic and Stochastic System Identification and Realization: DSR - A Subspace Approach Based on Observations [PDF]
Modeling, Identification and Control, 1996 A numerically stable and general algorithm for identification and realization of a complete dynamic linear state space model, including the system order, for combined deterministic and stochastic systems from time series is presented.
David Di Ruscio
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Eletrônica de Potência, 2020 This paper presents a steady-state analysis and design methodology for the Class-E² resonant DC/DC converter. The converter is represented in a normalized state-space model that is independent of specifications and real system components, like as ...
Lucas S. Mendonça +3 more
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State Space Methods inRATS [PDF]
Journal of Statistical Software, 2011 This paper uses several examples to show how the econometrics program RATS can be used to analyze state space models. It demonstrates Kalman filtering and smoothing, estimation of hyperparameters, unconditional and conditional simulation. It also provides a more complicated example where a dynamic simultaneous equations model is transformed into a ...
openaire +3 more sources
Fast robust methods for singular state-space models [PDF]
Automatica, 2019 11 pages, 4 ...
Jonker J. +4 more
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Identification and data-driven model reduction of state-space representations of lossless and dissipative systems from noise-free data [PDF]
, 2011 We illustrate procedures to identify a state-space representation of a lossless- or dissipative system from a given noise-free trajectory; important special cases are passive- and bounded-real systems.
Trentelman, Harry +9 more
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Assessing performance of Bayesian state-space models fit to Argos Satellite telemetry locations processed with Kalman Filtering [PDF]
, 2014 Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS)
Silva, Monica A. +25 more
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On the Efficacy of State Space Reconstruction Methods in Determining Causality [PDF]
SIAM Journal on Applied Dynamical Systems, 2015 Summary: We present a theoretical framework for inferring dynamical interactions between weakly or moderately coupled variables in systems where deterministic dynamics plays a dominating role. The variables in such a system can be arranged into an interaction graph, which is a set of nodes connected by directed edges wherever one variable directly ...
Bree Cummins +2 more
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