DocumentCode
295875
Title
Fuzzy aggregation networks of hybrid neurons with generalized Yager operators
Author
Keller, James M. ; Yang, Hong
Author_Institution
Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA
Volume
5
fYear
1995
fDate
Nov/Dec 1995
Firstpage
2270
Abstract
There have been several models of neural network structures which incorporate fuzzy set theoretic connectives in the activation functions of the nodes. Fuzzy aggregation networks (FANs) are a flexible and trainable class of such networks. To date, FANs have used exponentially weighted inputs in the operators. In this paper, we introduce a linearly weighted version of the FAN which shows the same excellent decision-making potential as the more complex exponentially weighted predecessors
Keywords
fuzzy neural nets; fuzzy set theory; activation functions; decision-making potential; exponentially weighted inputs; fuzzy aggregation networks; fuzzy set theoretic connectives; generalized Yager operators; hybrid neurons; linearly weighted version; neural network structures; Computer networks; Decision making; Fans; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Neural networks; Neurons; Pattern recognition; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-2768-3
Type
conf
DOI
10.1109/ICNN.1995.487715
Filename
487715
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