一种新的多维稀疏规则模糊插值推理方法

经典的插值理论针对一维稀疏规则库的条件，提出了各种不同的插值方法，取得了很多很好的经验．但对多维稀疏规则条件的近似推理，研究很少，仅有的几种插值方法，存在着难以保证推理结果的凸性和正规性等问题．为了在多维稀疏规则条件下能得到好的插值推理结果。提出了一种基于几何相似的插值推理方法．该方法能较好地保证推理结果隶属函数的凸性和正规性，为智能系统中的模糊推理提供了一个十分有用的工具．

New Approach for Interpolative Reasoning with Multi-Dimensonal Sparse Fuzzy Rules

Many different interpolative reasoning approaches have been proposed to one dimension condition through interpolative reasoning theory in recent years. They are with many good experience, but the research for interpolative reasoning under multidimensional sparse rules condition is lacking and a few existing approaches have some faults. For example it is difficult to keep convexity and normality to them under multidimensional sparse rules condition. In order to get better conclusion under multidimensional sparse rules condition, we propose a based on geometric analogy multidimensional interpolative reasoning approach which can keep the convexity and normality of the reasoning conclusion better. It devotes a useful tool for fuzzy reasoning in intelligent systems.