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システム/制御/情報 Vol. 15 (2002), No. 12

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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キーワードランキング

16 May. (Last 30 Days)

システム/制御/情報 Vol. 15 (2002), No. 12

受動性に基づく非線形視覚フィードバック制御

丸山 章, 河合 宏之, 藤田 政之

pp. 627-635

DOI:

10.5687/iscie.15.627

抄録

This paper investigates the relative rigid body motion (positions and rotations) control problem with visual information. Firstly the model of the relative rigid body motion and the nonlinear observer are considered in order to derive the visual feedback system. Secondly stability and L2-gain performance analysis are discussed based on passivity. Finally we present simulation results to confirm the effectiveness of the proposed visual feedback control design.

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受動性に基づく非線形視覚フィードバック制御

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レベル制御を用いたアレーアンテナの等ミニマムレベル指向性

大久保 茂

pp. 636-642

DOI:

10.5687/iscie.15.636

抄録

In this paper, the new method of obtaining the radiation pattern having equal minimum levels of the linear array antenna. Procedures of this method are as follows : First, some specific directions in the radiation pattern and the minimum level are determined using level control method. Second, two specific directions on the adjoining sidelobes are almost assimilated. Finally, the amplitudes of the current are calculated using them. And the radiation pattern is nearly isotropic by this method. The radiation pattern can be obtained by controlling the amplitudes of the currents only. The amplitude of the currents can be designed by using the digitally controlled attenuators. As the attenuators can be accurately changed in steps of 0.03 dB or less, the resulting quantisation error will affect the radiation pattern. Then the effects of quantisation error on the radiation pattern are verified.

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レベル制御を用いたアレーアンテナの等ミニマムレベル指向性

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ファジィ理論およびベイズ理論の導入によるマルチクラスサポートベクトルマシンの汎化能力の向上

井上 拓也, 阿部 重夫

pp. 643-651

DOI:

10.5687/iscie.15.643

抄録

In this paper, to resolve unclassifiable regions in the support vector machines, we propose fuzzy support vector machines (FSVMs). Using the decision function obtained by training the SVM, for each class, we define a truncated polyhedral pyramidal membership function. Since, for the data in the classifiable regions, the classification results are the same, the generalization ability of the FSVM is the same as or better than that of the SVM. To further improve the generalization ability, we introduce the Bayes theory, assuming that the class distributions are normal, to optimize the bias term of the optimal hyperplane. We evaluate our methods for four benchmark data sets and demonstrate the superiority of the FSVM and Bayes FSVM over the SVM.

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ファジィ理論およびベイズ理論の導入によるマルチクラスサポートベクトルマシンの汎化能力の向上

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ソフトデータに基づく環境評価モデル

河野 小夜子, 領家 美奈, 中森 義輝, Yoshiteru NAKAMORI

pp. 652-662

DOI:

10.5687/iscie.15.652

抄録

This paper develops rule-based models of environmental evaluation based on a questionnaire survey to residents. Meaingful rules are obtained by utilizing the SC-Optimality algorithm that is one of methods of rule extraction in data mining. Rules reflect attiributes of each region or depend on the context. These results are more meaningful than those by existing researches and other methods.

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ソフトデータに基づく環境評価モデル

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欠測値を含むデータからの局所的な主成分の抽出法

本多 克宏, 神田 章裕, 市橋 秀友, 山川 あす香

pp. 663-672

DOI:

10.5687/iscie.15.663

抄録

In many real world applications data sets with missing values are quite common. In this paper, we propose a new approach which extracts local principal components for the feature extraction from a large scale database. The new method is based on a simultaneous approach to principal component analysis and fuzzy clustering with an incomplete data set including missing values. The simultaneous approach extracts local principal components by using the eigenvectors of the correlation coefficient matrix, while Fuzzy c-Varieties (FCV) proposed by Bezdek et al. uses the eigenvectors of the fuzzy scatter matrix.

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超凸多面領域を持つファジィクラシファイアの高速学習方式

滝川 知生, 阿部 重夫

pp. 673-680

DOI:

10.5687/iscie.15.673

抄録

In this paper, we discuss a fuzzy classifier with polyhedral regions. First, for each class we generate a hyperbox calculating the minimum and maximum values of the data belonging to the class. Next, we cut the hyperbox using training data belonging to the other classes so that class separability is maximized. Finally, for each convex polyhedron we define a membership function using the minimum operator. We demonstrate the superiority of our method over our previously developed classifier with polyhedral regions using thyroid, numeral, hiragana, and blood cell data sets.

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超凸多面領域を持つファジィクラシファイアの高速学習方式

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多段階交渉プロトコルの基本ダイナミズム評価

貝原 俊也

pp. 681-687

DOI:

10.5687/iscie.15.681

抄録

Generally social activities with human interaction contain adaptive solving mechanism on distributed resource allocation problems. In this paper we focus on one of the sociological multi-agent paradigm, multi-stage negotiation protocol (MSN), and try to analyse its basic behaviours, such as the effect of the number of agents and the effect of the uniformity of agents, in our virtual market model. The comparison with other popular method, named contract-net protocol (CNP), clarifies the cancellation effects in social negotiations with several experimental results.

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