Results 11 to 20 of about 852,124 (258)
Graphical State Space Model [PDF]
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
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
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]
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
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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Closed Form Solution of Synchronous Machine Short Circuit Transients
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]
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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A STATE SPACE MODEL OF THE ECONOMIC FUNDAMENTALS [PDF]
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Craine, Roger, Bowman, David
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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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State‐space models for optical imaging [PDF]
AbstractMeasurement of stimulus‐induced changes in activity in the brain is critical to the advancement of neuroscience. Scientists use a range of methods, including electrode implantation, surface (scalp) electrode placement, and optical imaging of intrinsic signals, to gather data capturing underlying signals of interest in the brain.
Kary L, Myers +2 more
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