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Transactions of the Institute of Systems, Control and Information Engineers Vol. 9 (1996), No. 5

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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  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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Transactions of the Institute of Systems, Control and Information Engineers Vol. 9 (1996), No. 5

A New Error Back Propagation Method for a Multilayer Neural Network and Its Algebraic Properties

Yoshihiro YAMAMOTO

pp. 201-209

DOI:

10.5687/iscie.9.201

Abstract

The back propagation method is well known as a supervised learning rule of a neural network.
In this paper, a new learning rule is proposed where the output error vector is adjusted to zero by correcting two kinds of vectors, the one is weighting vectors (matrix) and the other is an input vector of the layer. The corrected input vector has a role of a tentative teacher of the following layer. In this way, the output error is propagated backward, and is partly corrected by each weighting vector.
Computatinal method is also presented for a matrix inversion which is required in the proposed method and nonsingularity of the matix is discussed.
Simulation result of the Exclusive-OR problem shows the effectiveness of the proposed method.

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A New Error Back Propagation Method for a Multilayer Neural Network and Its Algebraic Properties

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Ris

Function Approximation by Fusing Its Different Order Derivatives

Hajimu KAWAKAKI, Yasuaki KUROE, Takehiro MORI

pp. 210-218

DOI:

10.5687/iscie.9.210

Abstract

Progress in recent sensor technology has inspired us to start research on recovering an object surface from depth data together with additional sensory information, i.e. data on its differential geometry or a priori knowledge about its smoothness. Any method for the recovery with both classes of the additional information, however, has not been proposed yet. Such a problem can be generalized to that of function approximation using its different order derivatives, which covers many important applications. To solve the problems generally, we propose a method of fusing all the information as follows : (1) To evaluate the smoothness and penalties for the derivative data of each order, we define a functional. (2) All the information are related to one another by restricting the fused results in the set of stationary functions of the functional. By the fusion, a new approximation is realized as the stationary function that minimizes all penalties simultaneously.

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Function Approximation by Fusing Its Different Order Derivatives

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Ris

Controller Design of Two Mass-Spring System via LMI

Toshiharu SUGIE, Kenichi HAMAMOTO

pp. 219-226

DOI:

10.5687/iscie.9.219

Abstract

This paper gives a controller design method of 2 mass-spring system which satisfies the given H∞ control performance and pole assignment in the prescribed region in the presence of physical parameter perturbations. In addition, its effectiveness is demonstrated by experiments.

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Controller Design of Two Mass-Spring System via LMI

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A Study of Estimating Vehicle Aerodynamics of Lift

Kakuro AMASAKA, Hiroyuki NAKAYA, Kazunori ODA, Tetsuya OHASHI, Shunji OSAKI

pp. 227-235

DOI:

10.5687/iscie.9.227

Abstract

It is shown that combining multivariate analysis with neural networks is useful for solving problems with complex interaction such as a vehicle aerodynamics of lift. This method leads us to various valuable observations. In this paper, we show the effectiveness of this method.
Regarding some of the past insufficiencies of solving problems by multivariate analysis method or neural networks only, there are conditions such as (1) difficult to collect data, (2) mere knowhow, (3) predicting complex interaction. But by combining neural networks method with multivariate analysis, we can solve the problems efficiently.
In addition, solving this problems by combining neural networks method with multivariate analysis, new detections about the works of hidden layers in neural networks were found.

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A Study of Estimating Vehicle Aerodynamics of Lift

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Loop Transfer Recovery Techniques for Time-Delay Systems with Non-Minimum Phase Lumped Part

Jingwei WU, Tadashi ISHIHARA, Hikaru INOOKA

pp. 236-245

DOI:

10.5687/iscie.9.236

Abstract

For a plant consisting of a lumped non-minimum phase part and an output delay, we discuss LTR (Loop Transfer Recovery) techniques for designing a predictor-based LQG controller. We focus our attention to the feedback property achieved at the plant output side. We show that a feedback property recovered by the formal application of the conventional LTR procedure can be achieved by the partial LTR technique which has a clear system-theoretic meaning. This fact provides a theoretical justification of the formal application of the conventional LTR technique to a plant including finite unstable zeros and a time delay. In addition, we point out that the partial LTR technique provides more freedom in shaping target feedback properties. We propose a simple technique exploiting this freedom. A numerical example is given to illustrate usefulness of the proposed technique.

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Loop Transfer Recovery Techniques for Time-Delay Systems with Non-Minimum Phase Lumped Part

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A System Configuration to Convert a Continuous Plant with Time Delay into a Linear Discrete Plant

Yoichi MORIKAWA, Yuji KAMIYA

pp. 246-248

DOI:

10.5687/iscie.9.246
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A System Configuration to Convert a Continuous Plant with Time Delay into a Linear Discrete Plant

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Influence of Microphone Dynamics on Sound Transmission Loss and Its Compensation

Teruyo WADA, Asako ISHIZAKI, Toshiro ONO, Yoshiyuki KUROBE

pp. 249-251

DOI:

10.5687/iscie.9.249
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Influence of Microphone Dynamics on Sound Transmission Loss and Its Compensation

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Development of the Conditions for the Delay-Independent Stabilization of Linear Systems

Takashi AMEMIYA

pp. 252-254

DOI:

10.5687/iscie.9.252
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Development of the Conditions for the Delay-Independent Stabilization of Linear Systems

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