Approximate Reasoning Based on IFRS and DS Theory With its Application in Threat Assessment
Yanli Lu, Xiaoli Lei, Zhiping Zhou, Yafei Song · IEEE Access · 2020
Threat assessment for aerial attack targets is an important aspect of air defense weapon systems in responding to multiple attacks. We establish a model based on intuitionistic fuzzy rough sets (IFRS) and D-S evidence theory for threat assessment from the required data with uncertainty in stages. Using the overall degree of dependency and attribute importance of the intuitionistic fuzzy information system as a heuristic function, we study algorithms to extract threat elements and rules based on IFRS, to generate an intuitionistic fuzzy rule base for threat assessment with degrees of belief and disbelief. Based on the threat assessment rule base, we study the BPA determination algorithm in multi-stage threat assessment. The intuitionistic fuzzy semantics of degree of belief in the rule conclusion are used to determine the focal elements corresponding to each aggregate rule, and to obtain the degree of support of the data in a stage for each threat level. A case study shows that, compared to a threat assessment method based solely on D-S evidence theory or intuitionistic fuzzy reasoning, the advantage of IFRS knowledge acquisition makes the selection of threat assessment elements and determination of BPA more objective and less dependent on domain experts, so as to yield strong, objective results.