Results 11 to 20 of about 687,122 (255)
Parameter and State Estimator for State Space Models [PDF]
The Scientific World Journal, 2014 This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state ...
Ruifeng Ding, Linfan Zhuang
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Predictive Control Based upon State Space Models [PDF]
Modeling, Identification and Control, 1989 Repetitive online computation of the control vector by solving the optimal control problem of a non-linear multivariable process with arbitrary performance indices is investigated.
Jens G. Balchen +2 more
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Standard State Space Models of Unawareness (Extended Abstract) [PDF]
Electronic Proceedings in Theoretical Computer Science, 2016 The impossibility theorem of Dekel, Lipman and Rustichini has been thought to demonstrate that standard state-space models cannot be used to represent unawareness. We first show that Dekel, Lipman and Rustichini do not establish this claim.
Peter Fritz, Harvey Lederman
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Wavelets in state space models [PDF]
Applied Stochastic Models in Business and Industry, 2003 AbstractIn this paper, we consider the utilization of wavelets in conjunction with state space models. Specifically, the parameters in the system matrix are expanded in wavelet series and estimated via the Kalman Filter and the EM algorithm. In particular this approach is used for switching models.
Zandonade, Eliana, Morettin, Pedro A.
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CoRR, 2023 This paper studies sequence modeling for prediction tasks with long range dependencies. We propose a new formulation for state space models (SSMs) based on learning linear dynamical systems with the spectral filtering algorithm (Hazan et al. (2017)).
Naman Agarwal +3 more
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Graphical State Space Model [PDF]
2021 IEEE International Conference on Unmanned Systems (ICUS), 2021 In this paper, a new framework, named as graphical state space model, is proposed for the real time optimal estimation of a class of nonlinear state space model. By discretizing this kind of system model as an equation which can not be solved by Extended Kalman filter, factor graph optimization can outperform Extended Kalman filter in some cases.
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Comparison Prediction of Transfer Function Models and State Space Models Using Fuzzy Method [PDF]
المجلة العراقية للعلوم الاحصائية, 2021 The research aims to build dynamic models represented by the transfer function and State Space Models of a single input variable and a single output variable, The input and output variables are represented by the temperatures of the water before the ...
Fahad Subhy, Heyam Hayawi
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Bayesian State Space Models in Macroeconometrics [PDF]
SSRN Electronic Journal, 2020 AbstractState space models play an important role in macroeconometric analysis and the Bayesian approach has been shown to have many advantages. This paper outlines recent developments in state space modelling applied to macroeconomics using Bayesian methods.
Joshua C.C. Chan, Rodney W. Strachan
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Comparison of prediction using Matching Pattern and state space models [PDF]
المجلة العراقية للعلوم الاحصائية, 2022 Predicting future behavior is one of the important topics in statistical sciences due to the need for it in different areas of life, and most countries rely on their development programs on advanced scientific foundations and methods in order to reach ...
heyam hayawi, najlaa saad
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Modeling Volatility Using State Space Models [PDF]
International Journal of Neural Systems, 1997 In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of
Jens Timmer, Andreas S. Weigend
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