| Budget Amount *help |
¥17,550,000 (Direct Cost: ¥13,500,000、Indirect Cost: ¥4,050,000)
Fiscal Year 2020: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥8,190,000 (Direct Cost: ¥6,300,000、Indirect Cost: ¥1,890,000)
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| Outline of Final Research Achievements |
We previously developed "PLM: Percolative Learning Method" which is a kind of learning method for layered deep neural networks. In this project, we studied theory, methods and applications of PLM. In PLM, we can "percolate" Aux data which are used only for learning into Main data which are used for learning and testing. We proved that PLM could achieve the precision rate higher than a conventional deep neural network experimentally. We dealt with time dependence data prediction and realization of software-sensor, and we showed that PLM is effective for various fields.
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