NETLA를 이용한 이진 신경회로망의 기하학적인 학습방법 = The Geometrical Learning Method of Binary Neural Network using NETLA

다국어 초록 (Multilingual Abstract)

This paper describes an optimal synthesis method of binary neural network(BNN) for an approximation problem of a circular region using a newly proposed learning algorithm[7]. Our object is to minimize the number of connections and neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm(NETLA) for the multilayer BNN. The synthesis method in the NETLA is based on the extension principle of Expanded and Truncated Learning(ETL) and is based on Expanded Sum of Product (ESP) as one of the boolean expression techniques. And it has an ability to optimize the given BNN in the binary space without any iterative training as the conventional Error Back Propagation(EBP) algorithm[6]. If all the true and false patterns are only given, the connection weights and the threshold values can be immediately determined by an optimal synthesis method of the NETLA without any tedious learning. Futhermore, the number of the required neurons in hidden layer can be reduced and the fast learning of BNN can be realized. The superiority of this NETLA to other algorithms was proved by the approximation problem of one circular region.

목차 (Table of Contents)

• Ⅰ.서론
• Ⅱ.본론
• 1.이진 신경회로망의 구조
• 2.은닉층내의 가중치와 임계치의 결정
• 3.ETL 알고리즘의 확장의 원리
• 4.실험결과
• Ⅲ.결론