A new interpolative reasoning method in sparse rule-based systems

In [7], Yan et al. analyzed Koczy and Hirota's linear interpolative reasoning method presented in [2,3] and found that the reasoning consequences by their method sometimes become abnormal fuzzy sets. Thus, they pointed out that a new interpolative reasoning method will be needed which can guarantee that the interpolated conclusion will also be triangular-type for a triangular-type observation. In this paper, we extend the works of [2,3,7] to present a new interpolative reasoning method to deal with fuzzy reasoning in sparse rule-based systems. The proposed method can overcome the drawback of Koczy and Hirota's method described in [7]. It can guarantee that the statement "If fuzzy rules A(1) double right arrow B-1, A(2) double right arrow B-2, and the observation A* are defined by triangular membership functions, the interpolated conclusion B* will also be triangular-type" holds. (C) 1998 Elsevier Science B.V.