Automatic construction of fuzzy graphs for function approximation

Function approximation using 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 rule set based on a global grid that covers the whole input space, a different approach is presented in this paper. A constructive algorithm finds a locally independent rule set that forms a fuzzy graph. The proposed algorithm builds the fuzzy graph from scratch, without the need to control additional parameters. First results show promising performance and robustness against noise on an artificial dataset.