Possibilistic Evaluation of Extended Fuzzy Rules in the Presence of Uncertainty

Characterization fuzzy in term "fuzzy rule base" is currently referred to the ability to define rule antecedents using fuzzy numbers. On the other hand, when it comes to the knowledge described by the rules and to the information contained in rule antecedents, absolute accuracy is assumed. With the emergence of a vast variety of applications of rule based systems, where antecedents are not provided by sensors but rather by complicated processing modules, more efficient rules and rule evaluation structures are needed, that are able to describe knowledge in more intuitive manner and cope with uncertainty in the assumed input. In this paper we propose extended fuzzy rules that allow for optional antecedents and provide a methodology for the possibilistic evaluation of both conventional and extended fuzzy rules in the presence of uncertainty. The work has been successfully applied in a real life problem, for which conventional fuzzy rules and fuzzy rule evaluation were inadequate