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システム/制御/情報 Vol. 13 (2000), No. 6

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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  25. Vol. 13 (2000)

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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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キーワードランキング

22 Dec. (Last 30 Days)

システム/制御/情報 Vol. 13 (2000), No. 6

ペンシル行列の擬似対角化アルゴリズム

柴里 弘毅, 汐月 哲夫, 川路 茂保

pp. 258-267

DOI:

10.5687/iscie.13.6_258

抄録

Recently a pencil model has attracted attention again in view of behavioral approach. It is well known that a pencil can be transformed into a canonical form, but the traditional numerical computation method is implemented in terms of pivoting operation. In this paper, we make it clear what kind of mode the invariant subspace corresponds to, and propose a new algorithm to transform the pencil into the quasi-diagonal form. Since the method is based on geometric approach, it has an advantage of numerical stability.

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ペンシル行列の擬似対角化アルゴリズム

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自然災害リスクの軽減を目的とした意思決定分析のモデル化

田村 坦之, 山本 浩嗣, 赤沢 克洋, 田地 宏一

pp. 268-275

DOI:

10.5687/iscie.13.6_268

抄録

In this paper, we show that a value function under risk is useful to model low probability and high consequence damage events like an earthquake for which expected utility theory is inadequate. Firstly, we assume alternatives to improve buildings, some scenarios of earthquakes, costs to improve buildings, probability of death and injury and cost of restoring building's damage for each scenario. Then, we show that the value function under risk is an appropriate approach to model and analyze decision making process with low probability and high consequence events.

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自然災害リスクの軽減を目的とした意思決定分析のモデル化

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パターン認識用三層ニューラルネットの不等式の求解による高速学習方式

土屋 直樹, 小澤 誠一, 阿部 重夫

pp. 276-283

DOI:

10.5687/iscie.13.6_276

抄録

In this paper we discuss training of three-layered neural network classifiers by solving inequalities. Namely, first we represent each class by the center of the training data belonging to the class, and determine the set of hyperplanes that separate each class (i.e., each center) into a single region. Then according to whether the center is on the positive or negative side of the hyperplane, we determine the target values of each class for the hidden neurons (i.e., hyperplanes). Since the convergence condition of the neural network classifier is now represented by the two sets of inequalities, we solve the sets successively by the Ho-Kashyap algorithm. We demonstrate the advantage of our method over the backpropagation algorithm using several benchmark data sets.

mendeley

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パターン認識用三層ニューラルネットの不等式の求解による高速学習方式

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移動体搭載レーザスキャナによる移動体の3次元位置姿勢計測法

小松 信雄, 津村 俊弘, 大久保 博志

pp. 284-292

DOI:

10.5687/iscie.13.6_284

抄録

This paper proposes a new method for measuring the position and attitude of an autonomous land vehicle navigating in a rough terrain. The measurement system proposed here provides precise information on position and heading quickly and continuously. It consists of two laser scanners mounted on the vehicle and corner cubes placed in the environment as landmarks. Each laser scanner rotates a fan-shaped laser beam for detecting the retro-reflections by the corner cubes and measures their azimuth angles. This paper presents the principle of measurement and method of positional and attitude estimation. Simulation results are given to show the accuracy of the proposed method. This paper reports the measurement principle, simulation and experimental results.

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移動体搭載レーザスキャナによる移動体の3次元位置姿勢計測法

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適合性確率に基づくモデル集合の検証

宮里 智樹, 原 辰次, 周 〓

pp. 293-299

DOI:

10.5687/iscie.13.6_293

抄録

We characterize a probabilistic measure named Model Set Unfalsified Probability (MSUP) for model set validation, where the model set is described by an LFT (Linear Fractional Transformation) form. We derive upper and lower bounds of MSUP and show that the lower bound computation can be reduced to an LMI-based convex optimization. A necessary and sufficient condition for which MSUP=0.5 (50%) is also provided. A numerical example confirms that the probabilistic approach more appropriately evaluates the suitability of a model set in robust controller design than deterministic approaches.

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適合性確率に基づくモデル集合の検証

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