대한기계학회논문집A (Transactions of the Korean Society of Mechanical Engineers A)

  • 제26권6호
  • /
  • Pages.1092-1098
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  • 2002
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  • 1226-4873(pISSN)
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  • 2288-5226(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
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실시간 진화 알고리듬을 통한 신경망의 적응 학습제어

Adaptive Learning Control of Neural Network Using Real-Time Evolutionary Algorithm

  • 장성욱 (부산대학교 기계공학부 대학원) ;
  • 이진걸 (부산대학교 기계기술연구소)
  • 발행 : 2002.06.01
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초록

This paper discusses the composition of the theory of reinforcement teaming, which is applied in real-time teaming, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line teaming method. The individuals are reduced in order to team the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It is possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because of the teaming process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

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참고문헌

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