声望模型中一种动态选择遗忘因子的方法

Method of dynamically selecting forgetting factor in reputation model

针对目前声望模型中单一的遗忘因子无法准确地跟踪动态变化的声望值的问题,提出了一种以降低总误差为目标的动态选择遗忘因子的方法。该方法首先分析了不同的遗忘因子对总误差的影响,然后以声望值的变化程度为依据,在变化较为剧烈时选择较大的遗忘因子以快速体现变化,在变化较小时选择较小的遗忘因子以减小随机误差。仿真结果表明:该方法是行之有效的。

In order to address the problem that most current reputation models cannot track the change of reputation accurately, this paper proposes a method of dynamically selecting forgetting factor to minimize the total error.Mter analyzing the influence of different forgetting factor on total error,it is based on the change degree of reputation to select forgetting factor.When reputation changes sharply,it selects the bigger forgetting factor to track the change quickly.Otherwise,it selects the smaller forgetting factor to reduce the random error.The simulation results demonstrate that this method is effective.