The Journal of the Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회논문지)

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Design of Optimized Pattern Recognizer by Means of Fuzzy Neural Networks Based on Individual Input Space

개별 입력 공간 기반 퍼지 뉴럴 네트워크에 의한 최적화된 패턴 인식기 설계

  • 박건준 (원광대학교 정보통신공학과) ;
  • 김용갑 (원광대학교 정보통신공학과) ;
  • 김변곤 (군산대학교 전자공학과) ;
  • 황근창 (원광대학교 반도체.디스플레이학과)
  • Received : 2013.01.22
  • Accepted : 2013.04.12
  • Published : 2013.04.30
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Abstract

In this paper, we introduce the fuzzy neural network based on the individual input space to design the pattern recognizer. The proposed networks configure the network by individually dividing each input space. The premise part of the networks is independently composed of the fuzzy partition of individual input spaces and the consequence part of the networks is represented by polynomial functions. The learning of fuzzy neural networks is realized by adjusting connection weights of the neurons in the consequent part of the fuzzy rules and it follows a back-propagation algorithm. In addition, in order to optimize the parameters of the proposed network, we use real-coded genetic algorithms. Finally, we design the optimized pattern recognizer using the experimental data for pattern recognition.

본 논문에서는 패턴 인식기를 설계하기 위하여 개별 입력 공간을 기반으로 한 퍼지 뉴럴 네트워크를 소개한다. 제안된 퍼지 뉴럴 네트워크는 각 입력 공간을 개별적으로 분할함으로서 네트워크를 구성한다. 규칙의 전반부는 개별적 입력 공간을 퍼지 분할하여 독립적으로 구성하고, 규칙의 후반부는 다항식으로서 표현된다. 퍼지 뉴럴 네트워크의 학습은 퍼지 규칙의 후반부에 있는 뉴런의 연결가중치를 조정함으로써 실현되고, 오류 역전파 알고리즘을 이용하여 실현한다. 또한, 제안한 네트워크의 파라미터를 최적화하기 위하여 실수 코딩 유전자 알고리즘을 이용한다. 마지막으로, 패턴 인식을 위한 실험 데이터를 이용하여 최적화된 패턴 인식기를 설계한다.

Keywords

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