A New Method of Fuzzy Interpolative Reasoning Based on Gaussian-Type Membership Function

When rule base is sparse, we cannot get any reasoning result by traditional CRI method for an observation is in the gap between two neighboring antecedents. Ko¿czy and Hirota have proposed a linear interpolative reasoning method, which give a solution for the problem, so fuzzy interpolative reasoning was born. But now, all of the interpolative reasoning methods are almost based on triangular-type membership function, little based on Gaussian-type membership function. Therefore, in this paper, a new method of fuzzy interpolative reasoning based on the proportion of vertex and inflection point of Gaussian-type membership function will be presented, which based on the method of linear interpolative reasoning. It provides a useful tool with fuzzy interpolative reasoning.