| Project/Area Number |
15K18095
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Control engineering/System engineering
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| Research Institution | Waseda University |
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Principal Investigator |
Parque Victor 早稲田大学, 理工学術院, 准教授(任期付) (50745221)
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | learning / networks / graph representation / optimization / network design / product design / path planning / evolutionary computing / learning networks / design / control / evolutionary computation / representations / combinatorial / pattern recognition / succinctness / computer-aided design / modularity / succint representation / robotics |
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| Outline of Final Research Achievements |
In this research, learning networks with succinct structures and their applications were conducted. In one hand, succinct structures for directed networks, undirected networks, modular networks and variable-size networks achieving the state of the art efficiency were made possible. And the applications in path planning and CAD problems have shown the excellent performance, efficiency and scalability of the proposed algorithms.
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| Academic Significance and Societal Importance of the Research Achievements |
Succinct learning networks bring the unique benefits of representational economy, algorithmic versatility and conceptual clarity when encoding richer and complex knowledge structures through graphs, rendering improved approaches when tackling complex problems in control and design.
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