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Transactions of the Institute of Systems, Control and Information Engineers Vol. 18 (2005), No. 5

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

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  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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  16. Vol. 22 (2009)

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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)

    1. No.12
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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
    6. No.2
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Keyword Ranking

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 18 (2005), No. 5

Delayed Feedback Control Resolving Difficulty in Stabilization by Manual Control

Takashi TAKIMOTO, Shigeru YAMAMOTO

pp. 165-170

DOI:

10.5687/iscie.18.165

Abstract

Generally, limitations of human accuracy in manual manipulation hinder the quality of work performed by human operators of manual control systems. In a system controlled by manual manipulation, unnecessary vibration in the system caused by the movements of the operator is undesirable. Therefore, effective suppression of undesired motion is required to enhance the accuracy of control systems. In this paper, we show that additional feedback control based on a delayed feedback mechanism is effective to suppress harmful oscillatory motions in such systems.

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Delayed Feedback Control Resolving Difficulty in Stabilization by Manual Control

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Ris

An AR Model Identification Method using a Prior Information and Its Application to a Spectral Estimation of Speech Signal

Ken NAKAMURO, Teruyo WADA, Sueo SUGIMOTO

pp. 171-177

DOI:

10.5687/iscie.18.171

Abstract

A novel Auto Regressive (AR) model parameter estimation method is proposed, which can utilize a prior information as well as time series data, by extending the Burg method on the basis of the Minimum Cross Entropy (MCE) principle. As a practical application of the proposed method, we consider an approach to spectral estimation of speech data. In general, effectiveness of a prior information to spectral estimation results depends on the variation of speech signal. Thus we introduce an algorithm to determine the usage of a prior information, based on the divergence measure defined by the Kullback information. Finally, the estimation results for real speech data illustrate improved performance in comparison to the Burg method.

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An AR Model Identification Method using a Prior Information and Its Application to a Spectral Estimation of Speech Signal

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Ris

EM Algorithm for State-space Identification with Observation Outliers

Jaafar ALMUTAWA, Hideyuki TANAKA, Tohru KATAYAMA

pp. 178-186

DOI:

10.5687/iscie.18.178

Abstract

This paper considers an improvement of estimates of a state space model obtained by subspace system identification methods via the EM algorithm in the presence of observation outliers. To initialize the EM algorithm the initial estimates are obtained by two subspace identification methods : MOESP [1] and ORT [2]. The E- and M-steps in the EM algorithm are calculated when outliers are detected, by computing the conditional expectation under the assumption that the output data is incompletely observed. The outliers are detected and deleted in the EM algorithm by a simple scheme in robust statistics by using the median of the residuals, which are defined as the difference between the observed outputs and the estimated outputs computed from the initial estimates by subspace methods. Numerical examples show that the EM algorithm can monotonically improve the initial estimates obtained by subspace identification methods.

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EM Algorithm for State-space Identification with Observation Outliers

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Minimax Estimation of Uncertain Systems in the Presence of Bounded Disturbance

Wataru KITAMURA, Yasumasa FUJISAKI

pp. 187-189

DOI:

10.5687/iscie.18.187
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Minimax Estimation of Uncertain Systems in the Presence of Bounded Disturbance

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