A neural network approach to the fuzzy transform
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. (C) 2008 Elsevier B.V. All rights reserved.
neural network fuzzy transform learning fuzzy approximation rbf