JICCE (한국정보통신학회)
Current Result Document :
| 한글제목(Korean Title) |
Performance Improvement of Backpropagation Algorithm by Automatic Tuning of Learning Rate using Fuzzy Logic System |
| 영문제목(English Title) |
Performance Improvement of Backpropagation Algorithm by Automatic Tuning of Learning Rate using Fuzzy Logic System |
| 저자(Author) |
Kyung-Kwon Jung
Joong-Kyu Lim
Sung-Boo Chung
Ki-Hwan Eom
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| 원문수록처(Citation) |
VOL 01 NO. 03 PP. 0157 ~ 0162 (2003. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
We propose a learning method for improving the performance of the backpropagation algorithm. The proposed method is using a fuzzy logic system for automatic tuning of the learning rate of each weight. Instead of choosing a fixed learning rate, the fuzzy logic system is used to dynamically adjust the learning rate. The inputs of fuzzy logic system are delta and delta bar, and the output of fuzzy logic system is the learning rate. In order to verify the effectiveness of the proposed method, we performed simulations on the XOR problem, character classification, and function approximation. The results show that the proposed method considerably improves the performance compared to the general backpropagation, the backpropagation with momentum, and the Jacobs'delta-bar-delta algorithm. |
| 키워드(Keyword) |
Backpropagation
Fuzzy logic system
Automatic tuning of learning rate
Delta-bar-delta
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| 파일첨부 |
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