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Transactions of the Institute of Systems, Control and Information Engineers Vol. 7 (1994), No. 1

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)

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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. 7 (1994), No. 1

Sensor Diagnosis of Process Plants by an Immune-Based Model

Yoshiteru ISHIDA, Francois MIZESSYN

pp. 1-8

DOI:

10.5687/iscie.7.1

Abstract

Several algorithms on the immune network information model, which can be applied to sensor fault diagnosis of process plants, is proposed, and they are evaluated by simulations. The sensor network can eliminate the information of abnormal sensors by mutual recognition.
In cement processes, the reliabilities of sensors, on which the diagnostic system is based, are often far from perfection. The proposed sensor network can be used in such situation that the diagnosis of the diagnostic system itself becomes critical. The usefulness of the sensor network is confirmed by the simulation for the firing section of a cement plant.

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Sensor Diagnosis of Process Plants by an Immune-Based Model

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Ris

Existence Conditions of State Feedback in Discrete Event Systems under Partial State Observation

Shigemasa TAKAI, Toshimitsu USHIO, Shinzo KODAMA

pp. 9-17

DOI:

10.5687/iscie.7.9

Abstract

In this paper, we consider state feedback control of discrete event systems under partial state observation. A control specification is assumed to be given in terms of a predicate on the set of states. First, we introduce a new definition of observability of predicates, which is a generalization of the definition proposed by Li and Wonham. We then show necessary and sufficient conditions for the existence of a state feedback which achieves the control specification. Next, we introduce the notion of η-observability, which is a natural extention of observability, and present necessary and sufficient conditions for the existence of a decentralized state feedback which achieves the control specification.

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Existence Conditions of State Feedback in Discrete Event Systems under Partial State Observation

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Ris

Manipulator Trajectory Control by Momentum Change Inverse Models Using Multilayer Neural Networks

Katsuhiro HORI, Masaki KAGEYAMA, Takeshi TSUCHIYA

pp. 18-25

DOI:

10.5687/iscie.7.18

Abstract

This paper proposes a learning method of inverse manipulator dynamics model using only position and velocity. The direct inverse modeling method that was proposed as a learning method using neural network requires sensing manipulator position, velocity, and acceleration, because this method is formularized on the basis of manipulator. motion equation. However, since it is difficult at present to sense accurately manipulator acceleration, we could hardly implement this method by original formula. In the momentum change inverse modeling; the learning method that we proposed in this paper, manipulator motion causality is modeled not on the basis of manipulator motion equation but on the manipulator momentum change equation. With this formulation, sensing acceleration becomes unnecessary, inverse manipulator dynamics model can be learned using sensible position and velocity.

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Manipulator Trajectory Control by Momentum Change Inverse Models Using Multilayer Neural Networks

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The Effect of Synaptic Disconnection on the Performance of Hopfield Associative Memories

Mehdi NOURI SHIRAZI, Mahdad NOURI SHIRAZI, Sadao MAEKAWA

pp. 26-31

DOI:

10.5687/iscie.7.26

Abstract

Hopfield associative memories (HAMs) consisting of n (-1, +1) -binary neurons are considered. Each synaptic connection is assumed to be disconnected with probability q. Using the Galambos Poison limit theorem of order statistics, it is shown rigorously that HAMs with up to n2q randomly scattered synaptic disconnections have, with high probability, region of attractions of size ρn (ρn-error-correction capability) provided that they are not loaded with more than (1-2ρ) 2 (1-q) n/2log (1-q) n2 encoded patterns.

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The Effect of Synaptic Disconnection on the Performance of Hopfield Associative Memories

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Two-Degree-of-Freedom Optimal Servosystems for Plants with Delay

Yasumasa FUJISAKI, Masao IKEDA, Yuzou KUBOYAMA

pp. 32-34

DOI:

10.5687/iscie.7.32
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Two-Degree-of-Freedom Optimal Servosystems for Plants with Delay

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