| Budget Amount *help |
¥15,470,000 (Direct Cost: ¥11,900,000、Indirect Cost: ¥3,570,000)
Fiscal Year 2021: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2020: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2019: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2018: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
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| Outline of Final Research Achievements |
While control systems that utilize large amounts of data and machine learning methods are expected to have nonlinearity and the ability to flexibly respond to changes in the environment, the lack of theoretical performance guarantees that have been ensured using mathematical models so far makes it difficult to dispel vague concerns when it comes to industrial applications. In this study, we developed the data-driven model reduction theory proposed by the principal investigator and derived theoretical results for de-black-boxing machine learning-assisted methods by giving them a stochastic control theoretical interpretation.
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