输入信息不完整的置信规则库推理方法

Belief rule-base inference methodology with incomplete input

摘要:

现有的关于置信规则库的研究大多集中于参数优化问题上,而对于数据采集困难导致输入信息不完整,从而使得置信规则库系统难以正常运行的问题研究较少.为了使置信规则库系统能在输入信息不完整的情况下继续运行,提出了一种输入信息不完整的置信规则库推理方法.首先,从经验或利用数据统计的方法从历史数据获取前提条件属性分布情况；然后,对于缺失的前提条件属性利用分层抽样的方法得到多个候选值,并与其他前提条件属性组合成多个输入,对每个输入分别利用置信规则库推理方法进行推理；最后利用ER算法将所有输入的推理结果进行融合.在汽车发动机故障诊断的仿真实验中,将本方法与其他几种方法进行对比分析.实验结果表明:本方法在有充足历史数据的情况下,推理累积误差明显小于其他方法.而且通过多组实验,发现本方法对不同的分布情况有很好的适应性；本方法在尽量降低计算复杂度的基础上减少了推理误差,给不完整输入信息的置信规则库推理提供了一种新思路.

Abstract:

Most of the existing researches on belief rule-base inference methodology focus on the problem of parameter optimization, while few on the problem that the belief rule-base system cannot operate normally due to the incomplete input information caused by the difficulty of data acquisition. In order to solve this problem, an inference method based on incomplete input is proposed. First, the precondition attributes distribution was obtained from experience or historical data using statistical method. Then, the step sampling method was used to obtain multiple candidate values for the missing precondition attribute, which was combined with other precondition attributes. Each of the inputs was inferred using the belief rule-base inference methodology. Finally, the ER algorithm was used to fuse all the input inference results. In the simulation experiment of automobile engine fault diagnosis, the method of this paper was compared with other methods. Results show that the cumulative inference error of the proposed method was obviously smaller than other methods when there was sufficient historical data. Moreover, through multiple experiments, it was found that the proposed method has good adaptability to different distributions. This method reduces the inference error on the basis of minimizing the computational complexity, and provides a new idea for the belief rule-base inference of incomplete input information.