基于异常值的拟态裁决优化方法

针对拟态裁决器多数一致性表决算法的优化方法,提出用异常检测的方法直接量化数据可靠性来提升表决正确率.基于异常值的表决算法,通过构建拟态系统异构执行体输出数据集和训练深度学习异常检测模型量化了执行体输出数据异常值;使用权值优化算法优化加权分配,在表决时选择最优加权结果作为表决输出结果.实验结果表明,该方法能够提升拟态裁决器的表决输出正确率,具有一定共模逃逸检测能力,提升了系统的安全性和可靠性.

Mimic ruling optimization method based on executive outliers

This paper proposed an optimization method for majority consensus voting algorithm to improve the security of mimic adjudicators. This mimic ruling method based on executive outliers used anomaly detection to directly quantify data reliability to improve voting accuracy. Through constructing the executive body output data set and training the deep learning model, it quantified the abnormal value of the executive body output data. Then, it used the weight optimization algorithm to optimize the weighted distribution of the two parameters. Finally, it selected the optimal weighted result as the voting output result during voting. The experimental results show that the proposed method can improve the accuracy of mimic voting output and has a certain common mode escape detection capability. The optimization model can significantly improve the safety performance of the system.