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Learning and Generalization in Adaptive Fuzzy Logic Networks

We introduce the adaptive fuzzy logic network (AFLN) as the appropriate architecture for the realization of fuzzy membership and fuzzy logic functions. The according AFLN to a given training set is easily constructed and optimized. As their main advantage AFLNs provide for a very eecient sequential implementation of fuzzy systems. This eeciency is achieved by lazy evaluation and the application of a special windowing scheme.