Inference Methods for Partially Redundant Rule Bases

This paper discusses inference strategies for fuzzy rule bases resulting from databased automatic rule generation algorithms. Typical methods for rule generation are tree-oriented, statistical and evolutionary approaches. The aim of these data-based methods is the design of compact rule bases with a small number of interpretable rules which map the learning data set and provide a su cient statistical soundness. Powerful methods use linguistic hedges as at least, approximately or di erent abstraction levels of linguistic terms as positive for an abstract description or positive small and positive large for a more specialized description. In addition, many rule conditions only specify some linguistic variables with linguistic terms. The resulting rule bases are often characterized by partially redundant rules with identical conclusions, rules with overlapping premises and contradictory conclusions, disjunctive combinations of linguistic terms and missing rules for untrained input combinations. Any fuzzy inference strategy should produce the results expected by human experts reading the rules and membership functions. Classical inference strategies and fuzzy operators partially give strange results if some of the speci c characteristics of automatically generated rule bases occur. A rst task is the formalization of these expected results into so-called semantic constraints. Secondly, modi ed inference approaches will be proposed to more adequate results. The aims of this paper are