| Project/Area Number |
09450065
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
設計工学・機械要素・トライボロジー
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| Research Institution | The University of Tokyo |
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Principal Investigator |
YOSHIMURA Shinobu UNIV.TOKYO, INSUTITUTE OF ENVIRONMENTAL STUDIES, PROFESSOR, 大学院・新領域創成科学研究科, 教授 (90201053)
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| Co-Investigator(Kenkyū-buntansha) |
KANTO Yasuhiro TOYOHASHI UNIV.TECH., ASSOC.PROFESSOR, 機械システム工学系, 助教授 (60177764)
HORIE Yomoyoshi KYUSHU INSTITUTE OF TECH., SCHOOL OF INFROMATION ENG., ASSOC.PROFESSOR, 情報工学部, 助教授 (40229224)
YAGAWA Genki UNIV.TOKYO,SCHOOL OF ENG., PROFESSOR, 大学院・工学系研究科, 教授 (40011100)
FURUKAWA Tomonari UNIV.TOKYO, SCHOOL OF ENG., ASSOC.PROFESSOR, 大学院・工学系研究科, 講師 (10272395)
塩谷 隆二 九州大学, 工学部, 講師 (70282689)
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥14,400,000 (Direct Cost: ¥14,400,000)
Fiscal Year 1999: ¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 1998: ¥3,000,000 (Direct Cost: ¥3,000,000)
Fiscal Year 1997: ¥9,200,000 (Direct Cost: ¥9,200,000)
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| Keywords | NEURAL NETWORK / SENSORY INFORMATION / AUTOMOBILES / HANDLING AND STABILITY / SUBJECTIVE EVALUATION / STATISTICAL ANALYSIS / SATISFACTORY DESIGN / ニューラルネットワーク / ファジィ集合論 / アァジィ集合論 |
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| Research Abstract |
In this research, we develop a new method for multi-objective satisfactory design of artifacts such as automobiles. In the method, we first specify a certain quantitative level of sensory information such as subjective evaluation for handling and stability of vehicle, and then determine sets of design variables which satisfy the level of design requirement. Another key issue here is how to derive a quantitative relationship between sensory information and physical response.
In the first year, we performed experiments on subjective evaluation for handling and stability of vehicles in MAZDA's driving course using about 30 kinds of commercial cars. We invented a neural network-based method to derive the quantitative relationship and satisfactory design solutions using the relationship. The method is successfully applied to the experimental data. In addition, we developed a software system to automatically perform a series of data processes which runs in the Windows environment. Using the system, the processing time can be dramatically shortened. In the last year, we also implemented a recurrent type neural network to enhance learning capability. The system is applied some other types of problems such as prediction of corrosion life time, and promising results are obtained.
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