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

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Author(s)

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, 1999-09-30

    The Society of Instrument and Control Engineers

References:  12

Codes

  • NII Article ID (NAID)
    10004576938
  • NII NACSIS-CAT ID (NCID)
    AN00072392
  • Text Lang
    JPN
  • Article Type
    ART
  • ISSN
    04534654
  • NDL Article ID
    4858153
  • NDL Source Classification
    ZM11(科学技術--科学技術一般--制御工学)
  • NDL Call No.
    Z14-482
  • Data Source
    CJP  NDL  J-STAGE 
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