Training controllers for robustness: multi-stream DEKF
作者： L.A. Feldkamp , G.V. Puskorius
关键词: Extended Kalman filter 、 Artificial intelligence 、 Invariant extended Kalman filter 、 Kalman filter 、 Algorithm 、 Covariance intersection 、 Fast Kalman filter 、 Machine learning 、 Ensemble Kalman filter 、 Computer science 、 Robustness (computer science) 、 Alpha beta filter
摘要: Kalman-filter-based training has been shown to be advantageous in many applications. By its nature, extended Kalman filter (EKF) is realized with instance-by-instance updates, rather than by performing updates at the end of a batch instances or patterns. Motivated originally desire able base an update collection instances, just one, we recognized that simple construct multiple streams examples allows batch-like performed without violating underlying principle training, vis. approximate error covariance matrix remain consistent have actually performed. In this paper, present and show how it may used train robust controllers, i.e. controllers perform well for range plants. >
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