Fuzzy input-output controllers are universal approximators
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fuzzy transforms: Theory and applications
Fuzzy Sets and Systems
F-transform with parametric generalized fuzzy partitions
Fuzzy Sets and Systems
Fuzzy transform and least-squares approximation: Analogies, differences, and generalizations
Fuzzy Sets and Systems
A linguistic approach to time series modeling with the help of F-transform
Fuzzy Sets and Systems
Approximation properties of fuzzy transforms
Fuzzy Sets and Systems
Fuzzy projection versus inverse fuzzy transform as sampling/interpolation schemes
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Finitary solvability conditions for systems of fuzzy relation equations
Information Sciences: an International Journal
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The paper deals with the F-transform technique which was introduced as a method for an approximate representation of continuous functions. The same task is solved by many methods from different areas. Neural networks also belong to techniques which have been proved to be powerful for approximation objectives. They provide us with many advantages, especially incremental way of learning parameters. The paper inherits neural approaches to the F-transform method and presents experiments justifying the proposed approach. Incremental way of determination of certain parameters of the F-transform method which were up to now given by batch formula enriches possible areas of application of the method by fast on-line processes. Moreover, the neural approach is demonstrated to be an appropriate one for finding such fuzzy partition of a domain which respects better a given set of measured samples which are to be approximated by a continuous function with no predetermined shape.