Interval Certitude Rule Base Inference Method using the Evidential Reasoning

Abstract

Development of rule-based systems is an important research area for artificial intelligence and decision making, as rule base is one of the most general purpose forms for expressing human knowledge. In this paper, a new rule-based representation and its inference method based on evidential reasoning are presented based on operational research and fuzzy set theory. In this rule base, the uncertainties of human knowledge and human judgment are designed with interval certitude degrees which are embedded in the antecedent terms and consequent terms. The knowledge representation and inference framework offer an improvement of the recently developed rule base inference method, and the evidential reasoning approach is still applied to the rule fusion. It is noteworthy that the uncertainties will be defined and modeled using interval certitude degrees. In the end, an illustrative example is provided to illustrate the proposed knowledge representation and inference method as well as demonstrate its effectiveness by comparing with some existing approaches.

Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

“…In addition, the Dempster rule can efficiently combine the sensor reports from different sources [79]. Due to the desirable properties, evidence theory has been accepted as de facto standard in decision making [10,28,78], risk and reliability analysis [13,27,40], system optimization [67] and pattern recognition [23,24,34,46,57].…”