Abstract
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 the memory and computational demands also increase drastically, and more stringently fitting problems may occur if the number of training data is limited. The approach presented in this paper addresses these two problems by decomposing the functional mapping into the Network of Fuzzy Adaptive Nodes (NetFAN). This decomposition reduces the number of parameters as well as memory and computational demands. Basic characteristics of the NetFAN approach are outlined.
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