A knowledge acquisition method for fuzzy expert systems in diagnosis problems

In this paper a method for knowledge acquisition in diagnosis problems is presented. This method results in a zero-order Sugeno rule base where the combinatorial explosion of rules is solved by a decomposition scheme. This approach allows a unified representation, where the knowledge obtained from data by a supervised learning algorithm can be directly confronted with the knowledge elicited from the experts. The supervised learning algorithm is tested upon some classification problems found in literature.