Similarity interpolative reasoning for the sparse fuzzy rule

Fuzzy reasoning is really equal to a interpolation. But when rule base is sparse, we cannot get any reasoning consequence by traditional CRI method when an observation is in the gap between two neighboring antecedents. It is also difficult to remain convexity and normality using KH linear interpolative reasoning method. In order to get better consequence when rule base is sparse, we propose a similarity interpolative reasoning method that can remain the convexity and normality of the reasoning consequence better. It devotes a useful tool for fuzzy reasoning in intelligent systems.