We present an efficient method for selecting important input variables when building a fuzzy model from data. Past methods for input variable selection require generating different models while searching for the optimal combination of variables; our method requires generating only one model that employs all possible input variables. To determine the important variables, premises in the fuzzy rules of this initial model are systematically removed to search for the best simplified model without actually generating any new models. We also present an efficient method for generating the initial model that typically must incorporate a large number of input variables. These methods are illustrated through application to the benchmark Box and Jenkins gas furnace data; the results are compared with those of other fuzzy models found in literature.
['Stephen Chiu']
Selecting Input Variables for Fuzzy Models