Abstract
This paper discusses a system of self-organizing maps that approximate the fuzzy membership function for an arbitrary number of fuzzy classes. This is done through the ordering and clustering properties of one-dimensional self-organizing maps and iterative approximation of conditional probabilities of nodes in one map being the winner given that a node in the other map is the winner. Application of this system reduces fuzzy membership design time to that required to train the system of self-organizing maps.
Recommended Citation
T. E. Sandidge and C. H. Dagli, "Derivation of Fuzzy Membership Functions using One-Dimensional Self-Organizing Maps," Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 1997, Institute of Electrical and Electronics Engineers (IEEE), Jan 1997.
The definitive version is available at https://doi.org/10.1109/ICSMC.1997.638077
Meeting Name
IEEE International Conference on Systems, Man, and Cybernetics, 1997
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
1D Self-Organizing Maps; Conditional Probability; Function Approximation; Fuzzy Membership Functions; Fuzzy Set Theory; Iterative Method; Iterative Methods; Learning; Learning (Artificial Intelligence); Probability; Self-Organising Feature Maps
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1997 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 1997
