Title :
Representing acquired knowledge of neural networks by fuzzy sets: control of internal information of neural networks by entropy minimization
Author :
Kamimura, Ryotaro ; Yager, Ronald R. ; Nakanishi, Shohachiro
Author_Institution :
Inf. Sci. Lab., Tokai Univ., Kanagawa, Japan
Abstract :
The authors propose an entropy algorithm to extract the internal information of the neural networks, and show that the extracted information is expressed by fuzzy sets. Fuzzy sets representing internal information of neural networks after learning are composed of the competitive hidden unit activities which can be controlled by the entropy method. We apply this method to meaning interpretation of alphabet
Keywords :
entropy; fuzzy neural nets; fuzzy set theory; knowledge representation; learning (artificial intelligence); alphabet interpretation; competitive hidden unit; entropy minimization; fuzzy set theory; internal information; knowledge representation; learning; neural networks; Data mining; Entropy; Fuzzy sets; Information science; Knowledge acquisition; Machine intelligence; Machine learning; Minimization methods; Neural networks; Shape;
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
DOI :
10.1109/FUZZY.1994.343689
