Results 11 to 20 of about 2,446,518 (308)
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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A Learning State-Space Model for Image Retrieval
EURASIP Journal on Advances in Signal Processing, 2007 This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature ...
Greg C. Lee +2 more
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Goal Model Evaluation Based on State-Space Representation
IEEE Access, 2020 Goal models have been used for the last two decades in various disciplines to represent business, organizations, and individuals' objectives. Several methodologies and standards have emerged, and various goal analysis and evaluation algorithms have been ...
Mohamed Abdel-Monem +2 more
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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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Estimating the Competitive Storage Model with Stochastic Trends in Commodity Prices
Econometrics, 2021 We propose a State-Space Model (SSM) for commodity prices that combines the competitive storage model with a stochastic trend. This approach fits into the economic rationality of storage decisions and adds to previous deterministic trend specifications ...
Kjartan Kloster Osmundsen +3 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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Closed Form Solution of Synchronous Machine Short Circuit Transients
ITB Journal of Engineering Science, 2010 This paper presents the closed form solution of the synchronous machine transients undergoing short circuit. That analytic formulation has been derived based on linearity and balanced conditions of the fault.
Gibson H.M. Sianipar
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Granger causality for state-space models [PDF]
Physical Review E, 2015 Granger causality, a popular method for determining causal influence between stochastic processes, is most commonly estimated via linear autoregressive modeling. However, this approach has a serious drawback: if the process being modeled has a moving average component, then the autoregressive model order is theoretically infinite, and in finite sample ...
Barnett, Lionel, Seth, Anil K.
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Model Uncertainty, State Uncertainty, and State-space Models [PDF]
, 2013 State-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition
Young, ER +5 more
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IEEE Open Access Journal of Power and Energy, 2021 The fault location problem has been tackled mainly through impedance-based techniques, the travelling wave principle and more recently machine learning algorithms. These techniques require both current and voltage measurement.
Nicolas Cifuentes, Bikash C. Pal
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