| Project/Area Number |
15K06077
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | Nippon Institute of Technology |
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Principal Investigator |
Jin'no Kenya 日本工業大学, 工学部, 教授 (50286762)
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| Co-Investigator(Kenkyū-buntansha) |
進藤 卓也 日本工業大学, 工学部, 助教 (90749110)
木村 貴幸 日本工業大学, 工学部, 准教授 (80579607)
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| Research Collaborator |
Hariya Yosuke
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 群知能 / 決定論システム / 非線形力学系 / 連続値最適化 / 機械学習 / PWMインバータ / 不変性 / 悪スケール性 / 粒子群最適化 / 非線形最適化 / 変数間依存性 / 回転不変性 / 決定論 / 非線形写像 / 進化計算 / 不良条件問題 / Levy分布 / 確率分布 / havy tail / 決定論的 / 力学系理論 / 正準系 |
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| Outline of Final Research Achievements |
We developed a nonlinear mapping optimization method with invariance based on analysis result of invariance of solution search performance due to variable dependency and ill-scale. Although the proposed algorithm is a deterministic system, local search capability improves and it has invariance. Based on the preliminary simulation results, we confirmed that the nonlinear mapping optimization method has higher search performance than the particle swarm optimization. When the proposed algorithm applies to machine learning, we confirmed that efficient learning result is obtained. The PWM inverter that is designed by using the proposed optimization method exhibits high power conversion efficiency.
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