论文标题
基于最少共同倍数及其属性分析的模糊近似推理方法
Fuzzy Approximate Reasoning Method based on Least Common Multiple and its Property Analysis
论文作者
论文摘要
本文显示了一种基于最小常见倍数（LCM）的新型模糊近似推理方法。它的基本思想是通过基于先行模糊集与随之而来的SISO模糊系统中的LCM的扩展距离度量获得新的模糊推理。提出的方法称为LCM。然后，本文分析了其某些属性，即还原性质，信息损失在推理过程中发生以及模糊控制的收敛性。理论和实验研究结果强调了提出的方法比以前的模糊推理方法有意义地改善了还原性能和信息损失和可控性。
This paper shows a novel fuzzy approximate reasoning method based on the least common multiple (LCM). Its fundamental idea is to obtain a new fuzzy reasoning result by the extended distance measure based on LCM between the antecedent fuzzy set and the consequent one in discrete SISO fuzzy system. The proposed method is called LCM one. And then this paper analyzes its some properties, i.e., the reductive property, information loss occurred in reasoning process, and the convergence of fuzzy control. Theoretical and experimental research results highlight that proposed method meaningfully improve the reductive property and information loss and controllability than the previous fuzzy reasoning methods.
