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Transactions of the Institute of Systems, Control and Information Engineers Vol. 33 (2020), No. 12

ISIJ International
belloff
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Backnumber

  1. Vol. 37 (2024)

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  2. Vol. 36 (2023)

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  3. Vol. 35 (2022)

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  4. Vol. 34 (2021)

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  5. Vol. 33 (2020)

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  6. Vol. 32 (2019)

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  7. Vol. 31 (2018)

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  8. Vol. 30 (2017)

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  9. Vol. 29 (2016)

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  10. Vol. 28 (2015)

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  11. Vol. 27 (2014)

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  12. Vol. 26 (2013)

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  13. Vol. 25 (2012)

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  14. Vol. 24 (2011)

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  15. Vol. 23 (2010)

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  17. Vol. 21 (2008)

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  18. Vol. 20 (2007)

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  19. Vol. 19 (2006)

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  20. Vol. 18 (2005)

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  22. Vol. 16 (2003)

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  23. Vol. 15 (2002)

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  24. Vol. 14 (2001)

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  25. Vol. 13 (2000)

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  26. Vol. 12 (1999)

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  27. Vol. 11 (1998)

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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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  31. Vol. 7 (1994)

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  32. Vol. 6 (1993)

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  33. Vol. 5 (1992)

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  34. Vol. 4 (1991)

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  35. Vol. 3 (1990)

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  36. Vol. 2 (1989)

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  37. Vol. 1 (1988)

    1. No.7
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    3. No.5
    4. No.4
    5. No.3
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Keyword Ranking

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 33 (2020), No. 12

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

Yasuaki Kaneda, Satoshi Suzuki, Yasuharu Irizuki

pp. 303-313

DOI:

10.5687/iscie.33.303

Abstract

Recently, in order to model systems with uncertainty, probabilistic modeling with conditional probability distribution are widely noticed. Modeling the systems as probability density function enables us to apply strategies considering unsertainty to control systems. One of the most famous modeling methods is Gaussian process regression (GPR). GPR is often used in the field of control systems and its effectiveness is demonstrated in many papers. However, since GPR can represent only Gaussian distribution, GPR cannot always achieve good performances for systems under nonGaussian noise. This paper focuses on kernel density estimation that can approximate arbitrary probability distributions and proposes a probabilistic modeling method for non-Gaussian systems including ones with dynamic characteristics. At that time, a proposed method assumes that measurement is written as a sum of noise and function. Moreover, a prior distribution of the function assumes to be Gaussian. Under the assumption, the proposed method estimates the function as hidden variables by variational Bayes methods. The proposed method can model arbitrary probability density function for single output systems from data. Numerical simulations demonstrate the effectiveness of the proposed method.

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

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Ris

Active Sensing and Control of a Mobile Robot Considering Uncertainly of Path Prediction

Tomohiro Kitaoka, Yuki Minami, Masato Ishikawa

pp. 314-316

DOI:

10.5687/iscie.33.314
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Active Sensing and Control of a Mobile Robot Considering Uncertainly of Path Prediction

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Reduction of Learning Time for Differential-Wheeled Mobile Robots by Knowledge Transfer for Real-Time Learning

Naoki Kotani

pp. 317-319

DOI:

10.5687/iscie.33.317
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Reduction of Learning Time for Differential-Wheeled Mobile Robots by Knowledge Transfer for Real-Time Learning

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