Results 231 to 240 of about 2,813,920 (263)
Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network. [PDF]
Entropy (Basel)Chen Z, Fu W, Zhang R, Fang Y, Xiao Z.
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Advancing state estimation for lithium-ion batteries with hysteresis through systematic extended Kalman filter tuning. [PDF]
Sci RepKnox J, Blyth M, Hales A.
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Networked SIRS model with Kalman filter state estimation for epidemic monitoring in Europe
Aalto A +4 more
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Communication Delay Outlier Detection and Compensation for Teleoperation Using Stochastic State Estimation. [PDF]
Sensors (Basel)Kim E +5 more
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Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia. [PDF]
PLOS Glob Public HealthAlyami L, Das S, Townley S.
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Harmonic State Estimation and Transient State Estimation
2006This thesis describes the algorithms and techniques developed for harmonic state estimation and transient state estimation, which can be used to identify the location of disturbance sources in an electrical power system. The previous harmonic state estimation algorithm is extended to include the estimation of time-varying harmonics using an adaptive ...
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Fundamenta Informaticae, 2008
In this paper we introduce a state-estimation method that uses a short-term memory to calculate the current state. A common way to solve state estimation problems is to use implementations of the Bayesian algorithmlike Kalman filters or particle filters. When implementing a Bayesian filter several problems can arise.
Jüngel, Matthias, Mellmann, Heinrich
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In this paper we introduce a state-estimation method that uses a short-term memory to calculate the current state. A common way to solve state estimation problems is to use implementations of the Bayesian algorithmlike Kalman filters or particle filters. When implementing a Bayesian filter several problems can arise.
Jüngel, Matthias, Mellmann, Heinrich
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Reinforcement Learning State Estimator
Neural Computation, 2007In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning ...
Jun, Morimoto, Kenji, Doya
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Vehicle State Estimation Based on Unscented Kalman State Estimation
2008 International Symposium on Computational Intelligence and Design, 2008This paper presents a method of estimating vehicle states using an Unscented Kalman filter (UKF). The UKF developed estimates Vehicle motion, such as yaw rate and side slip angle, from the noisy measurement set. The vehicle state estimation using a non-linear vehicle model with Unitire tire model will be compared to the measured state which is ...
Tianjun Zhu, Hongyan Zheng
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IEEE Transactions on Power Systems, 1998
Power system state estimation derives a real-time network model by extracting information from a redundant data set consisting of telemetered, predicted and static data items. This paper describes a generalized, fully developed, estimation approach that fundamentally improves the information extraction process.
O. Alsac +3 more
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Power system state estimation derives a real-time network model by extracting information from a redundant data set consisting of telemetered, predicted and static data items. This paper describes a generalized, fully developed, estimation approach that fundamentally improves the information extraction process.
O. Alsac +3 more
openaire +1 more source