| タイトル | Refining fuzzy logic controllers with machine learning |
| 本文（外部サイト） | http://hdl.handle.net/2060/19940027915 |
| 著者（英） | Berenji, Hamid R. |
| 著者所属（英） | NASA Ames Research Center |
| 発行日 | 1994-02-01 |
| 言語 | eng |
| 内容記述 | In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars. |
| NASA分類 | CYBERNETICS |
| レポートNO | 94N32421 |
| 権利 | No Copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/112288 |
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