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Research on Model of Circuit Fault Classification Based on Rough Sets and SVM

机译:基于粗糙集和支持向量机的电路故障分类模型研究

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摘要

Aiming at the characteristic of lacking swatches and paroxysmal faults, A fault classification model based on rough sets and SVM is put forward. The pretreatment of diagnosis data is constructed by attribute reduction in rough sets. Redundancy attribute is deleted from the diagnosis decision-making table without losing useful information, and the reduced diagnosis decision-making table is used as original training sets of classification sub-system. The dimension of fault symptom and the capability of classification is balanced. Finally an example shows the model is effective and reasonable.
机译:针对样本不足​​和阵发性故障的特点,提出了一种基于粗糙集和支持向量机的故障分类模型。诊断数据的预处理是通过对粗糙集进行属性约简来构建的。从诊断决策表中删除冗余属性,而不会丢失有用的信息,并将减少的诊断决策表用作分类子系统的原始训练集。故障症状的维数和分类能力是平衡的。最后通过实例说明了该模型的有效性和合理性。

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