研究者業績

上浦 尚武

カミウラ ナオタケ  (Naotake KAMIURA)

基本情報

所属
兵庫県立大学 大学院 工学研究科 教授
学位
博士(工学)(姫路工業大学)

J-GLOBAL ID
201801008648996860
researchmap会員ID
B000339805

論文

 232
  • Takanori Hashimoto, Teijiro Isokawa, Masaki Kobayashi, Naotake Kamiura
    Nonlinear Theory and Its Applications, IEICE 16(1) 197-207 2025年  
  • Daiki Fujimoto, Naotake Kamiura, Teijiro Isokawa, Nobuyuki Matsui, Tatsuaki Tsuruyama
    2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS) 1-6 2024年11月9日  
  • A.Inada, T.Isokawa, S.Nakade, F.Peper, Y.Utsumi, N.Kamiura
    Proc. of the 29th International Symposium on Artificial Life and Robotics 2024 (AROB 29th 2024) 662-666 2024年1月  査読有り
  • M.Kimura, T.Isokawa, F.Peper, S.Nakade, J.Lee, N.Kamiura
    Proceedings of 2023 11th International Symposium on Computing and Networking Workshops (CANDARW) 114-120 2023年11月  査読有り
  • Takanori Hashimoto, Nobuyuki Matsui, Naotake Kamiura, Teijiro Isokawa
    Journal of Advanced Computational Intelligence and Intelligent Informatics 27(4) 537-542 2023年7月20日  査読有り
    In this study, we investigate model structures for neural ODEs to improve the data efficiency in learning the dynamics of control systems. We introduce two model structures and compare them with a typical baseline structure. The first structure considers the relationship between the coordinates and velocities of the control system, while the second structure adds linearity with respect to the control term to the first structure. Both of these structures can be easily implemented without requiring additional computation. In numerical experiments, we evaluate these structure on simulated simple pendulum and CartPole systems and show that incorporating these characteristics into the model structure leads to accurate learning with a smaller amount of training data compared to the baseline structure.

MISC

 38

講演・口頭発表等

 21

所属学協会

 3

共同研究・競争的資金等の研究課題

 12

産業財産権

 2