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システム/制御/情報 Vol. 4 (1991), No. 7

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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  2. Vol. 36 (2023)

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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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  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)

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    3. No.5
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キーワードランキング

27 Dec. (Last 30 Days)

システム/制御/情報 Vol. 4 (1991), No. 7

複雑な構造をした非線形システムの同定を目的としたニューラルネットワーク

近藤 正

pp. 259-266

DOI:

10.5687/iscie.4.259

抄録

A neural network which can identify a nonlinear system whose structure is very complex, is described. This neural network is constructed with four layers. In the second layer of the neural network, nonlinear relationship of input variables are generated and quantized accurately. So, high order effects of input variables can be considered in the neural network. The neural network is applied to a nonlinear system identification problem and the results are compared with those which are obtained by using another neural network and GMDH (Group Method of Data Handling) algorithm.

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複雑な構造をした非線形システムの同定を目的としたニューラルネットワーク

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Ris

定性ネットワークモデルとそのシミュレーションアルゴリズム

堂園 浩, 石田 好輝, 得丸 英勝

pp. 267-276

DOI:

10.5687/iscie.4.267

抄録

We propose an algorithm for qualitative simulation of the large scaled physical systems such as plants. Such systems are often complicated for numerical simulation, thus the qualitative simulation is often used for analysis. We use the qualitative network model and implement a simulation algorithm modified for the qualitative network. This algorithm has three important futures : (1) devision of the propagation into two types, dynamic propagation and static propagations (2) balancing mechanism to avoid the undefined values, (3) rules for keeping consistencies on the network model.

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定性ネットワークモデルとそのシミュレーションアルゴリズム

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制御を前提とした設計手法とその車両操舵機構への応用

河辺 徹, 得丸 英勝

pp. 277-285

DOI:

10.5687/iscie.4.277

抄録

In this paper we present a new approach named the control configured design method. This method aims at designing a high performance machine system by combining a machine design and control, the concept of control configured vehicle (CCV). Performance index for this design method is defined in terms of ideal and actual inputs to the system. Applying the present method, we get feasible values of parameters for a car steering design.

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制御を前提とした設計手法とその車両操舵機構への応用

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拡散需要を伴う生産管理システムの最適制御

土肥 正, 海生 直人, 尾崎 俊治

pp. 286-293

DOI:

10.5687/iscie.4.286

抄録

So far many studies about the production management systems have dealt with demand under uncertainty, which is regarded as a time-independent random variable.In this paper, we discuss the production management systems when demand follows Markov diffusions. The stochastic control method is applied to the analytical derivation of the optimal production policies. Main objects in this paper are to extend a deterministic model in the earlier contributions to the stochastic one and further to clear the properties of control in the production management systems, considering the differences of the shapes between some demand distributions.

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拡散需要を伴う生産管理システムの最適制御

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協力環境下での学習オートマトンの集団モデル

銭 飛, 平田 廣則

pp. 294-301

DOI:

10.5687/iscie.4.294

抄録

In this paper, we consider a collective model of learning automata operating on N-static random environments. Each automaton has a behavioural tactic directed towards the realization of its own goal, estimating the magnitude of the expected value of utility function that depends explicity on the automaton's strategy and the corresponding responses from environments. From game theoretic viewpoint, we can construct several types of environments for this model.
In here, a non-cooperative game on N-cooperative environments is proposed. For this model, the definitions, evaluation metrics and a distance diminishing reinforcement scheme are introduced. As an application, a simple numerical example is shown.

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