활성화 함수의 이득 가변화를 이용한 역전파 알고리즘의 성능개선 = The Performance Improvement of Backpropagation Algorithm Using the Gain Variable of Activation Function

다국어 초록 (Multilingual Abstract)

In order to improve the several problems of the general backpropagation, we propose a method using a fuzzy logic system for automatic tuning of the activation function gain in the backpropagation. First, we researched that the changing of the gain of sigmoid function is equivalent to changing the learning rate, the weights, and the biases. The inputs of the fuzzy logic system were the sensitivity of error respect to the last layer and the mean sensitivity of error respect to the hidden layer, and the output was the gain of the sigmoid function.
In order to verify the effectiveness of the proposed method, we performed simulations on the parity problem, function approximation, and pattern recognition. The results show that the proposed method has considerably improved the performance compared to the general backpropagation.
Keywords: General backpropagation; Fuzzy logic system; Gain; Activation function; Parity problem; Function approximation; Pattern recognition.

목차 (Table of Contents)

• Ⅰ.서론
• Ⅱ.활성화 함수의 이득과 학습율, 연결강도 및 바이어스 사이의 관계
• Ⅲ.퍼지 논리를 이용한 시그모이드 함수의 이득 자동 조절 방식
• Ⅳ.시뮬레이션
• Ⅴ.결론