An inference engine based on Interval Type-2 Fuzzy BK subproduct

Chee Kau Lim, Chee Seng Chan

Abstract: Recently, Bandler-Kohout (BK) subproduct based reasoning scheme has been a popular choice in various kind of applications. In this paper, we aim to enhance the BK subproduct based reasoning schemes in two aspects: (1) Extend the BK subproduct in term of Interval Type-2 Fuzzy Sets (IT2FS) instead of the ordinary Type-1 Fuzzy Sets (T1FS), and (2) Introduce weight parameter to the reasoning scheme. Firstly, studies have shown that IT2FS have better capability in handling data with uncertainty compare to the ordinary T1FS. Thus, we extend the BK subproduct in terms of IT2FS theory where subsethood measure based on the fuzzy implication operators for the IT2FS has been developed. Secondly, weight parameter associated to each features is introduced to form a weighted inference scheme with the BK subproduct. The introduction of the weight parameter is to aid in distinguishing the influence of different features in the reasoning process. In here, the Linguistic Weighted Average (LWA) is adopted to solve the outputs of this weighted reasoning scheme. Finally, a case study is employed to demonstrate the capability of the proposed approach.