基于相互隶属度的云模型相似性度量方法

针对现有云模型相似性度量方法计算复杂度高、结果不稳定、适用性有限等问题，提出了一种基于相互隶属度的云模型相似性度量方法.依据云模型期望曲线以公共面积与自身面积之比定义云模型之间的隶属度，进而利用相互隶属度计算云模型之间的相似度.讨论了不同交点及半云情形下相应面积的计算方法，并给出了相似度的计算公式及性质证明.仿真实验表明，该方法能够在多种情形下合理地度量云模型的相似性，并且具有较高的结果准确度和较低的计算复杂度，基于云模型的评估实例进一步验证了该方法的有效性. 

Similarity Measurement Between Cloud Models Based on Mutual Membership Degree

A method of similarity measurement between cloud models was proposed based on mutual membership degree to solve the problems existing in current approaches,such as high consumption,unstable conclusion and limited applicability.Firstly,the membership degree of cloud models was defined with the ratio of common cloud area to itself cloud area according to expectation curve.Then,the similarity between cloud models was calculated with the mutual membership degree.Finally,the calculation formula and properties of similarity were produced,considering the calculation of the area with different intersection points and half cloud.Simulation results show that the proposed method can achieve rational similarity measurement between cloud models with high accuracy and low consumption in different situations.And the effectiveness of the method was further verified based on cloud model with an evaluation example.