Global Minimum Point Search Under Noisy Observations Using Estimator-Type Variable Hierarchical Structure Learning Automata

• MOGAMI Yoshio

  The University of Tokushima

• BABA Norio

  Osaka Educational University

• MATSUSHITA Masaki

  Mitsubishi Electric Corporation

Bibliographic Information

Other Title

• Estimator型可変階層構造学習オートマトンによるノイズを含む観測値を用いた大域的最小点探索
• システム/情報 Estimator型可変階層構造学習オートマトンによるノイズを含む観測値を用いた大域的最小点探索
• システム ジョウホウ Estimatorガタ カヘン カイソウ コウゾウ ガクシュウ オートマトン ニ ヨル ノイズ オ フクム カンソクチ オ モチイタ タイイキテキ サイショウテン タンサク

Abstract

The purpose of this paper is to construct a global optimization algorithm of the unknown multimodal objective function under noisy observations. Our algorithm is based on the learning performance of the variable hierarchical structure learning automata, and, in order to reduce the number of iterations, the estimator-type learning algorithm which is the rapdly converging one is used for the learning algorithm of the automata. The numerical experiment is carried out to verify the efficiency of the proposed algorithm, and, from the results, the proposed global optimization algorithm is useful for finding out a global minimum of the unkonwn multimodal objective function under noisy observations.

Journal

• Transactions of the Society of Instrument and Control Engineers

  Transactions of the Society of Instrument and Control Engineers 35 (9), 1191-1197, 1999

  The Society of Instrument and Control Engineers

Keywords

• hierarchical structure learning automata
• estimator-type learning algorithm
• optimization algorithm
• global minimum point
• noisy observation

Details

• CRID: 1390282679478324352
• NII Article ID: 10004576938
• NII Book ID: AN00072392
• DOI: 10.9746/sicetr1965.35.1191
• ISSN: 18838189
• ISSN: 04534654
• NDL BIB ID: 4858153
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I4858153
• Web Site: https://www.jstage.jst.go.jp/article/sicetr1965/35/9/35_9_1191/_pdf
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed