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システム/制御/情報 Vol. 7 (1994), No. 1

ISIJ International
belloff
オンライン版ISSN: 2185-811X
冊子版ISSN: 1342-5668
発行機関: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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システム/制御/情報 Vol. 7 (1994), No. 1

免疫型並列分散モデルによるプラントのセンサ自律診断

石田 好輝, Francois MIZESSYN

pp. 1-8

抄録

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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免疫型並列分散モデルによるプラントのセンサ自律診断

離散事象システムにおける部分観測のもとでの状態フィードバックの存在条件

高井 重昌, 潮 俊光, 児玉 慎三

pp. 9-17

抄録

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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離散事象システムにおける部分観測のもとでの状態フィードバックの存在条件

多層型ニューラルネットワークを用いた運動量変化逆モデル学習によるマニピュレータ軌道制御

堀 勝博, 陰山 正樹, 土谷 武士

pp. 18-25

抄録

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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多層型ニューラルネットワークを用いた運動量変化逆モデル学習によるマニピュレータ軌道制御

結合の故障がホップフィールド型連想記憶の性能に及ぼす影響について

前川 禎男, Mahdad NOURI SHIRAZI, Sadao MAEKAWA

pp. 26-31

抄録

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 ρnn-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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結合の故障がホップフィールド型連想記憶の性能に及ぼす影響について

むだ時間システムに対する2自由度積分型最適サーボ系

藤崎 泰正, 池田 雅夫, 窪山 祐蔵

pp. 32-34

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