A Hybrid Robust Identification Using Genetic Algorithm and Gradient Method

• HU Jinglu

  Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology

• KUMAMARU Kousuke

  Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology

• INOUE Katsuhiro

  Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology

書誌事項

タイトル別名

• Hybrid Robust Identification Using Gene

抄録

This paper deals with the issues related to developing an efficient system identification algorithm which may find “global minimum” of multimodal loss function robustly, on the basis of an effective combination of Genetic Algorithm (GA) and gradient method. In order to realize such robust system identification algorithm, a Non-Standard GA (NSGA) is proposed as an effective GA. In the NSGA, a new GA operator named as development is introduced to improve its convergent property. We can thus realize a hybrid robust identification, in which parameter estimation is executed by a gradient method based on a good initial value searched by the NSGA. The effectiveness of the proposed algorithm is demonstrated by numerical simulations.

収録刊行物

• 計測自動制御学会論文集

  計測自動制御学会論文集 32 (5), 714-721, 1996

  公益社団法人 計測自動制御学会

キーワード

• multimodality
• robust identification
• genetic algorithm
• gradient method

詳細情報

• CRID: 1390001204500806656
• NII論文ID: 130004712814
• NII論文ID: 10001764984
• NII書誌ID: AN00072392
• DOI: 10.9746/sicetr1965.32.714
• ISSN: 18838189
• ISSN: 04534654
• NDL書誌ID: 3958986
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I3958986
• Web Site: https://www.jstage.jst.go.jp/article/sicetr1965/32/5/32_5_714/_pdf
• 本文言語コード: en
• データソース種別:
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  • NDL
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  • CiNii Articles
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