Prediction of Discharge Current using Neural Network in Hall Thruster

• Fuchigami Hirotaka

  連絡先著者（Corresponding author）：[e-mail] 九州大学大学院総合理工学府先端エネルギー理工学専攻

• Chono Masatoshi

  九州大学大学院総合理工学府先端エネルギー理工学専攻

• Yamamoto Naoji

  九州大学大学院総合理工学研究院エネルギー科学部門

Bibliographic Information

Other Title

• ホールスラスタにおけるニューラルネットワークを用いた放電電流の予測
• ホールスラスタ ニ オケル ニューラルネットワーク オ モチイタ ホウデン デンリュウ ノ ヨソク

Abstract

We have been developing a prediction code of discharge current using neural network for constructing auto-controlling system in Hall thrusters. The neural network is feedforward neural network, which consists of 5 layers with 100 neurons. We adopted backpropagation method to the network and updated weights by AdaGrad. We used training 25500 data sets that consists of operation condition (inner and outer coil current, xenon mass flow rate, discharge voltage and time) and discharge current. The code could predict unknown discharge current history within relative error 1% with three days. The relative error with 2250 training data sets remains less than 1% within eight hours calculation on a standard PC. Considering actual operation, it is necessary to make learning speed up.

Journal

• JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES

  JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 66 (5), 143-145, 2018

  THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES

Keywords

• Hall Thruster
• Neural Network
• Supervised Learning

Details

• CRID: 1390001288076263424
• NII Article ID: 130007495906
• NII Book ID: AA11307372
• DOI: 10.2322/jjsass.66.143
• ISSN: 24323691
• ISSN: 13446460
• NDL BIB ID: 029302898
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I029302898
• Web Site: https://www.jstage.jst.go.jp/article/jjsass/66/5/66_66_143/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed