한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제20권5호
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  • Pages.672-676
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  • 2010
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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선택적 학습률을 활용한 학습법칙을 사용한 신경회로망

Fuzzy Neural Network Using a Learning Rule utilizing Selective Learning Rate

  • 백용선 (대덕대학 컴퓨터웹정보과) ;
  • 김용수 (대전대학교 컴퓨터공학과)
  • 투고 : 2010.07.27
  • 심사 : 2010.10.11
  • 발행 : 2010.10.25
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초록

본 논문은 연결강도를 조정할 때 결정 경계선 근처에 있는 데이터를 더 반영하는 학습법칙을 제안하였다. 이 학습법칙은 outlier가 결정 경계선에 미치는 영향을 줄여 더 나은 결정 경계선을 형성하도록 한다. 제안하는 학습법칙을 IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망의 구조에 적용하였다. IAFC 신경회로망은 배운 것을 유지하는 안정성이 있으면서, 새로운 것을 배울 수 있는 안정성이 있다. 이 퍼지 신경회로망의 성능과 LVQ(Learning Vector Quantization) 신경회로망 및 오류역전파 신경회로망의 성능과 비교하였다. 실험결과 제안하는 퍼지 신경회로망의 성능이 우수함을 보여주었다.

This paper presents a learning rule that weights more on data near decision boundary. This learning rule generates better decision boundary by reducing the effect of outliers on the decision boundary. The proposed learning rule is integrated into IAFC neural network. IAFC neural network is stable to maintain previous learning results and is plastic to learn new data. The performance of the proposed fuzzy neural network is compared with performances of LVQ neural network and backpropagation neural network. The results show that the performance of the proposed fuzzy neural network is better than those of LVQ neural network and backpropagation neural network.

키워드

참고문헌

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피인용 문헌

  1. Classification of Aroma Using Neural Network vol.23, pp.5, 2013, https://doi.org/10.5391/JKIIS.2013.23.5.431