I. Introduction
Fuzzy rule interpolation significantly improves the robustness of fuzzy reasoning. It provides a way to reduce the complexity of fuzzy systems by the omission of those rules that can be approximated by their neighboring ones. In addition, it improves the applicability of fuzzy systems by allowing a certain conclusion to be generated, even if the existing rule base does not cover a given observation. A number of important interpolating approaches have been presented in the literature, including [9], [12] [13] [31], [36]–[38], [42]–[45], [54], [56], [60], [63], and [64].