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.

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.

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