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J-GLOBAL ID:202002255241200656   Reference number:20A2811271

Probabilistic Modeling for Nonlinear and Non-Gaussian Systems Using Kernel Density Estimation and Gaussian Process

カーネル密度推定とガウス過程を用いた非線形・非ガウスシステムの確率的モデリング
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Material:
Volume: 33  Issue: 12  Page: 303-313  Publication year: Dec. 2020 
JST Material Number: L0070A  ISSN: 1342-5668  Document type: Article
Article type: 原著論文  Country of issue: Japan (JPN)  Language: JAPANESE (JA)
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System identification  ,  Statistical quality control  ,  System and control theory in general 
Reference (16):
  • [1] R. E. Kalman: A new approach to linear filtering and prediction problems; J. Basic Engineering, Vol. 82, No. 1, pp. 35-45 (1960)
  • [2] C. E. Rasmussen and C. K. I. Williams: Gaussian Processes for Machine Learning, The MIT Press (2006)
  • [3] J. Ko and D. Fox: GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models; Auton. Robot., Vol. 27, No. 1, pp. 75-90 (2009)
  • [4] J. Kocijan, R. Murray-Smith, C.E. Rasmussen and A.Girard: Gaussian process model based predictive control; Proc. of 2004 American Control Conference, pp.2214-2219 (2004)
  • [5] M. Maiworm, D. Limon, J. M. Manzano and R. Find eisen: Stability of Gaussian process learning based output feedback model predictive control; IFACPapersOnLine, Vol. 51, No. 20, pp. 455-461 (2018)
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