基于模糊贴近度的正态云模型相似度度量  

Similarity Measurement for Normal Cloud Models Based on Fuzzy Similarity Measure

作　　者：龚艳冰[1] 蒋亚东[1] 梁雪春[2] 

机构地区：[1]河海大学信息管理系,江苏常州213022 [2]南京工业大学系统工程研究所,江苏南京211816

出　　处：《系统工程》2015年第9期133-137,共5页Systems Engineering

基　　金：江苏省自然科学基金资助项目(BK20130242);河海大学中央高校基本科研业务费项目(2012B13814)

摘　　要：提出一种新的计算正态云模型相似度方法——基于组合模糊贴近度的正态云相似度方法,该方法利用两种模糊贴近度,并通过计算修正的正态云期望曲线的模糊贴近度度量云模型的相似度。实例表明,与现有方法相比本文方法计算简单,能够更有效的对正态云模型相似度进行度量。With normal cloud model in artificial intelligence,mapping between quantities and qualities becomes much easier and interchangeable.Normal cloud model integrates fuzziness and randomness of the qualitative concepts in the natural language.Based on the combined fuzzy similarity measurement,a new method to measure the similarity of normal cloud model is proposed which uses two kinds of fuzzy similarity measure and the normal cloud improved expectation curve to calculate the similarity measurement.A numerical example shows the new method is easier to calculate,feasible and effective in the normal cloud model similarity measurement.

关 键 词：正态云模型 相似性度量 期望曲线 模糊贴近度 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]