Hierarchical Fuzzy Rule Interpolation and its Application for Hotels Location Selection

Abstract

Fuzzy rule interpolation offers a useful means for enhancing the robustness of fuzzy models by making inference possible in systems of only a sparse rule base. However in practical applications, as the application domain of fuzzy systems expand to more complex ones, the curse of dimensionality problem of the conventional fuzzy systems became apparent, which makes the already challenging tasks such as inference and interpolation even more difficult. An initial idea of hierarchical fuzzy interpolation is presented in this paper. The proposed approach combines hierarchical fuzzy systems and fuzzy rule interpolation, to overcome the curse of dimensionality problem and the sparse rule base problem simultaneously. Hierarchical fuzzy interpolation is applicable to situations where a multiple multi-antecedent rules system needs to be reconstructed to a multi-layer fuzzy system and the sub-layer rules base is sparse. In order to demonstrate the potential of this approach, a hierarchical fuzzy decision making model for international tourist hotel location selection is provided in this paper. Criteria are acquired from literatures review and practical investigations for selecting the international tourist hotel location. These supportive systems can be directly presented to the tourists requesting a mechanism for selecting the most appropriate hotel, where lack enough information about the important indicators and factors. This model can also support the managers of hotels who are trying to make strategic decisions regarding the most optimized investments on the indicators of selecting a hotel. An empirical study for identifying the international tourist hotel location selection in Chongqing is conducted to demonstrate the computational results and effectiveness of the proposed methodology.