改进证据分类合成方法在农作物生长环境评估中的应用

摘要

Considering traditional D‐S evidence theory deficiencies existing in dealing with conflict evi‐dence ,the information entropy attribute is put forward based on the similarity between each evidences , which fixed the evidence classification properties .Combining the similarity attribute between each evi‐dences ,the evidence set could be divided into high credibility evidence ,general evidence and conflict evi‐dence .The sorted evidence set is given different importance coefficients ,and is modified to improve .Af‐ter modifying ,the general evidence and high conflict evidence are closed to the high credibility of evidence opinions .Finally ,D‐S combination rule is utilized to synthesis for the modified evidence .It is difficult for obtaining data by multiple sensors to establish the basic probability distribution function for the evidence . For the problem ,making full use of the ability that rough set theory can deal with incomplete information and knowledge ,the decision information table is obtained via the attribute reduction of rough set .The function values are assigned for the basic probability by the decision information table .Combining the rough set and the improved D‐S evidence theory ,the real data sets are measured by all kinds of sensors . The experimental results show that the improved method can not only effectively solve the conflict prob‐lem ,but also reduce the uncertainty of evidence .%针对传统D‐S证据理论存在处理冲突证据的不足，基于证据间的相似度引入了信息熵属性，修正了证据分类属性，结合证据间相似度属性将证据集重新划分为可信度高证据、一般性证据和冲突证据，对分类的证据集赋予不同的重要性系数，并加以修正改进。改进后使得一般性证据和高冲突证据向可信度高的证据意见靠拢，最后利用D‐S组合规则对于修正后的证据进行合成。针对农作物生长环境中多个传感器获取的数据构造其所对应证据的基本概率分配函数，利用模糊理论对基本概率分配函数进行取值。实验采用各类传感器测得的真实数据集进行实验，结果表明改进的方法既能够很好地解决冲突问题，同时能降低证据的不确定性。