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      하이브리드 신경회로망 알고리즘을 이용한 실시간 자율적응 제어 시스템 구현 = Implementation for Self-Adaptative Control System of Real Time using Algorithm of Hybrid Neural Network

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      https://www.riss.kr/link?id=A104715219

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      다국어 초록 (Multilingual Abstract)

      Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to by using single feedback neural network controller. it is difficult to get satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm of hybrid neural network controller combined with PID controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unified as activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is designed by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm.
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      Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to by using single feedback neural network controller. it is difficult to get satisfied performance when the changes of rapid load and...

      Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to by using single feedback neural network controller. it is difficult to get satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm of hybrid neural network controller combined with PID controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unified as activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is designed by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm.

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      목차 (Table of Contents)

      • Ⅰ. 서 론
      • Ⅱ. 하이브리드 신경회로망 제어 시스템
      • Ⅲ. 실험 및 결과고찰
      • Ⅳ. 결 론
      • Ⅰ. 서 론
      • Ⅱ. 하이브리드 신경회로망 제어 시스템
      • Ⅲ. 실험 및 결과고찰
      • Ⅳ. 결 론
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