Building fuzzy graphs from examples

Function approximation based on example data has gained considerable interest in the past. The automatic extraction of a fuzzy rule base has proven to be a powerful tool to build approximators that allow an interpretation of the underlying model. In contrast to most known systems which find a set of rules based on a global grid that covers the whole input space, a different approach is presented in this paper. A constructive algorithm finds a set of local individual rules forming a fuzzy graph. The proposed algorithm builds the fuzzy graph from scratch, without the need to control additional parameters and shows promising performance and robustness against noise on an artificial dataset