| Project/Area Number |
16510130
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Konan University |
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Principal Investigator |
NAKAYAMA Hirotaka Konan University, Department of Information Science and Systems Engineering, Professor, 理工学部, 教授 (20068141)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,800,000 (Direct Cost: ¥3,800,000)
Fiscal Year 2006: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2005: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2004: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | support vector machines / support vector regression / sequential approximate optimization / metamodelling / cable stayed bridges / seismic reinforcement / black box objective functions / computational intelligence / ブラックボックス目的関数最適化 / 応答曲面法 / 近似最適化 / 斜張橋耐震補強 / 遺伝アルゴリズム |
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| Research Abstract |
In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective functions is obtained by real/computational experiments such as structural analysis, fluidmechanic analysis, thermodynamic analysis, and so on.
Usually, these experiments are considerably expensive. In order to make the number of these experiments as few as possible, optimization is performed in parallel with predicting the form of objective functions. Response Surface Methods (RSM) are well known along this approach.
This research proposes several approaches to RSM such as Radial Basis Function Networks (RBFN) and Support Vector Machines (SVM). One of the most important tasks in this approach is to find effective sample data moderately in order to make the number of experiments as small as possible. In the proposed methods, additional sample data are selected in such a way that both global information for better approximation of objective function and local information for more precise approximation of optimal solution are added.
In this research, in particular, a new type of support vector regression (SVR) called μ-v-SVR is proposed along the line of multi-objective optimization (MOP) and goal programming (GP).
Several methods are compared along with not only test problems but also real bridge examples.
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