A proposal for improving the accuracy of linguistic modeling

Abstract

We propose accurate linguistic modeling, a methodology to design linguistic models that are accurate to a high degree and may be suitably interpreted. This approach is based on two main assumptions related to the interpolative reasoning developed by fuzzy rule-based systems: a small change in the structure of the linguistic model based on allowing the linguistic rule to have two consequents associated; and a different way to obtain the knowledge base based on generating a preliminary fuzzy rule set composed of a large number of rules and then selecting the subset of them best cooperating. Moreover, we introduce two variants of an automatic design method for these kinds of linguistic models based on two well-known inductive fuzzy rule generation processes and a genetic process for selecting rules. The accuracy of the proposed methods is compared with other linguistic modeling techniques with different characteristics when solving of three different applications.