Optimal Synthesis of Binary Neural Network using NETLA

NETLA를 이용한 이진 신경회로망의 최적합성

  • 정종원 (동아대학교 대학원 전기공학과) ;
  • 성상규 (한국항공우주산업 (주)) ;
  • 지석준 (동아대학교 대학원 전기공학과) ;
  • 최우진 (동아대학교 대학원 전기공학과) ;
  • 이준탁 (동아대학교 전기 전자 ·컴퓨터 공학부)
  • Published : 2002.05.01

Abstract

This paper describes an optimal synthesis method of binary neural network(BNN) for an approximation problem of a circular region and synthetic image having four class using a newly proposed learning algorithm. Our object is to minimize the number of connections and neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm(NETLA) based on the multilayer BNN. The synthesis method in the NETLA is based on the extension principle of Expanded and Truncated Learning (ETL) learning algorithm using the multilayer perceptron and is based on Expanded Sum of Product (ESP) as one of the boolean expression techniques. The number of the required neurons in hidden layer can be reduced and fasted for learning pattern recognition.. The superiority of this NETLA to other algorithms was proved by simulation.

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