一种新的基于综合相似度的区间值模糊推理方法

摘要

在深入分析已有的基于相似度的模糊推理算法的基础上，针对其计算复杂、未考虑规则前件权重及规则激活方法可能导致结论不全面的问题，提出一种基于综合相似度的区间值模糊推理算法。引入综合相似度、规则阈值、规则可信度等概念，给出新的模糊规则激活方法及推理结论可信度的计算方法。通过算例说明了新方法在多重多维模糊推理的情况下具有还原性，且计算简单，适用于实际应用。%Based on deep analysis on existing similarity-based fuzzy reasoning algorithms, we put forward an interval-valued fuzzy inference algorithm, which is based on comprehensive similarity, to tackle the problems of complicated calculation, neglecting the weight of all premises of fuzzy rules, and the possible incomplete conclusions led by rules activation method. This algorithm introduces the concepts of comprehensive similarity, rules thresholds and rules reliability degree, etc.Furthermore, it presents a new activation method for fuzzy rules and the calculation method of reliability degrees of reasoning conclusion.It is proved through numerical example that the new algorithm has the reductive property under the circumstance of multiple and multidimensional fuzzy inference, and its calculation is simple, thus it is suitable for practical applications.