中文摘要：

针对现有证据信任模型中普遍存在的局部信任度计算对门限值敏感度高及信任主体不能准确理解推荐信任度的含义等引起的模型性能下降问题,该文提出一种基于模糊修正的证据信任模型,一方面,通过采用模糊成员函数对信任评价进行加权处理,使得局部信任度随着门限值的变化而渐变变化,可以有效避免实质性突变的发生;另一方面,该文提出了一种推荐信任度修正算法使得推荐信任度的实际意义能够被信任主体准确理解,提高了信任度量的准确性。仿真实验表明,与已有证据信任模型相比,该文模型具有较强的抗干扰和抗攻击能力,能适用于各种动态环境中,同时在度量的精准度方面也有较大提高。

英文摘要：

In current evidential trust models,there exist some deficiencies such as local trust degree is sensitive to the threshold and the exact meaning of recommendation trust degree for trust target provided by recommender are obscure to the trust source,so an evidential trust model is proposed with fuzzy adjustment method to solve these problems.On the one hand,the trust ratings are adjusted through applying fuzzy sets,which makes local trust degree changes with the change parameter gradual and can avoid the occurrence of the mutation effectively.On the other hand,the exact meaning of recommendation trust degree can be comprehended through constructing a adjust method for the recommendation trust degree,which improve the accuracy of the trust metric calculated from the recommendation trust values.The simulation results show that compared with existing evidential trust models,the trust model in this paper has a strong ability in anti-disturbance and anti-attack,which is applied to a variety of dynamic environments,and has more remarkable enhancements in the accuracy of trust measurement.