NetFAN - A Structured Adaptive Fuzzy Approach

Olaf Huwendiek, Werner Brockmann


Adaptive fuzzy systems are useful universal function approximators. But they suffer from the curse of dimensionality. I.e. the number of parameters, which have to be tuned, increases drastically if the number of input variables increases. This has the effect that memory and computational demands increase also drastically, and more stringently fitting problems may occur if the number of training data is limited. The approach presented in this paper addresses both problems by decomposing the functional mapping into a Network of Fuzzy Adaptive Nodes (NetFAN). This decomposition reduces the number of parameters as well as memory and computational demands. Some first investigations outline the basic characteristics of the NetFAN-approach.

Original languageEnglish
Title of host publicationProceedings of International Conference on Neural Networks (ICNN'96)
Number of pages6
Publication date01.01.1996
ISBN (Print) 0-7803-3210-5
Publication statusPublished - 01.01.1996
EventProceedings of the 1996 IEEE International Conference on Neural Networks - Washington, United States
Duration: 03.06.199606.06.1996
Conference number: 45420


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