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Nonlinear Function Approximation of Moduled Neural Network Using Genetic Algorithm

  • 박현철 (중앙대학교 전자전기공학부) ;
  • 김성주 (중앙대학교 전자전기공학부) ;
  • 김종수 (중앙대학교 전자전기공학부) ;
  • 서재용 (한국기술교육대학교 정보기술공학부) ;
  • 전홍태 (중앙대학교 전자전기공학부)
  • 발행 : 2001.12.01

초록

Nonlinear Function Approximation of Moduled Neural Network Using Genetic Algorithm Neural Network consists of neuron and synapse. Synapse memorize last pattern and study new pattern. When Neural Network learn new pattern, it tend to forget previously learned pattern. This phenomenon is called to catastrophic inference or catastrophic forgetting. To overcome this phenomenon, Neural Network must be modularized. In this paper, we propose Moduled Neural Network. Modular Neural Network consists of two Neural Network. Each Network individually study different pattern and their outputs is finally summed by net function. Sometimes Neural Network don't find global minimum, but find local minimum. To find global minimum we use Genetic Algorithm.

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