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针对使用传统模糊综合评判方法进行故障级别评判时模型参数难以确定的问题,提出一种基于分布估计算法(EDA)的模糊综合故障评判方法.该方法利用EDA进行模糊评判模型的进化学习,能有效实现模糊模型参数的自动优化,并具有模型易于理解、计算效率高的优点.通过对磁浮列车悬浮系统的仿真实验,结果显示基于分布估计算法的模糊故障综合评判方法能获得优于传统进化算法和其他机器学习方法的评判效果,具有较好的应用价值.
Aiming at the problem that the model parameters can not be easily determined by the traditional fuzzy comprehensive evaluation method, a fuzzy comprehensive fault evaluation method based on the distribution estimation algorithm (EDA) is proposed. This method uses EDA to make evolutionary learning of the fuzzy evaluation model and is effective Which can realize the automatic optimization of the parameters of the fuzzy model and has the advantages of easy to understand model and high computational efficiency.Through the simulation experiment of the maglev train suspension system, the results show that the fuzzy fault comprehensive evaluation method based on the distribution estimation algorithm can get the better performance than the traditional evolutionary algorithm Other machine learning methods to judge the effect, has good application value.