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weighted mean roughness
相关语句
  加权平均粗糙度
     Fault Diagnosis Layer Model of Distribution Network Based on Weighted Mean Roughness
     基于加权平均粗糙度的配电网故障诊断分层模型
短句来源
     We presented weighted mean roughness, a new concept based on rough sets theory which is regarded as the criteria for choosing attributes.
     基于粗糙集的理论提出了加权平均粗糙度的概念 ,将其作为选择分离属性的标准。
短句来源
     Then the decision tree of electric power grid diagnosis is built by using weighted mean roughness as separating attributes standard to realize electric power grid fault diagnosis.
     然后采用加权平均粗糙度的概念,作为选择分离属性的标准,构造电网故障决策树,从而实现对电网的故障诊断。
短句来源
     To improve the indeterminacy and imperfection of electric power fault information that cause the difficulty to obtain accuracy fault diagnosis results, a method is proposed in which by use of improved discernible matrix the fault samples are reduced and the reduced samples are layered according to the value of weighted mean roughness, thereby a fault diagnosis layer model is built.
     针对配电网发生故障后故障诊断警报信息存在不确定性和不完整性导致难以得出准确结论的问题,提出了利用改进可辨识矩阵对故障样本进行约简,在约简的基础上根据加权平均粗糙度的大小对故障样本进行分层从而建立模型的方法。
短句来源
     With the proposed method the blindness and redundancy of layering can be avoided and the space for diagnosis model is evidently diminished. In addition the weighted mean roughness is easy to be calculated and implemented.
     该方法避免了分层的盲目性和冗余性,大大缩小了诊断模型空间,且加权平均粗糙度计算简单,易于实现。
短句来源
  “weighted mean roughness”译为未确定词的双语例句
     According to the value of weighted mean roughness the priority of different characteristic information can be distinguished, so the proposed method possesses strong tolerant ability.
     按加权平均粗糙集的大小区分层内各特征信息的优先级,具有很强的容错能力。
短句来源
  相似匹配句对
     On the Weighted Mean of L-Function
     关于L-函数的一个加权均值
短句来源
     ON THE WEIGHTED MEAN INEQUALITY
     关于加权平均值不等式
短句来源
     Weighted Sorting
     加权排序法
短句来源
     Mean Entropy
     平均熵
短句来源
     Fault Diagnosis Layer Model of Distribution Network Based on Weighted Mean Roughness
     基于加权平均粗糙度的配电网故障诊断分层模型
短句来源
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In the process of constructing a decision tree, the criteria of selecting partitional attributes will influence the efficiency of classification. We presented weighted mean roughness, a new concept based on rough sets theory which is regarded as the criteria for choosing attributes. The experiments show that, compared with the entropy-based method, our method is simpler in the structure, and can improve the efficiency of classification.

在构造决策树的过程中 ,分离属性选择的标准直接影响分类的效果。基于粗糙集的理论提出了加权平均粗糙度的概念 ,将其作为选择分离属性的标准。经实验证明 ,用该方法构造的决策树与传统的基于信息熵方法构造的决策树相比较 ,复杂性低 ,且能有效提高分类效果。

By employing decision tree to build the model of electric power grid fault diagnosis,fault sample with non numerical and inaccuracy values can be processed,diagnosis result can be learned when information is corrupted,erroneous and even missing because of its strong fault tolerant ability and better adaptive capability.Based on rough set theory,the paper proposes an improved discernibility matrix to reduce the decision table which includes all kinds of fault cases with the signals of protection relays and takes...

By employing decision tree to build the model of electric power grid fault diagnosis,fault sample with non numerical and inaccuracy values can be processed,diagnosis result can be learned when information is corrupted,erroneous and even missing because of its strong fault tolerant ability and better adaptive capability.Based on rough set theory,the paper proposes an improved discernibility matrix to reduce the decision table which includes all kinds of fault cases with the signals of protection relays and takes circuit breakers as condition attributes and fault sections as value attributes.Then the decision tree of electric power grid diagnosis is built by using weighted mean roughness as separating attributes standard to realize electric power grid fault diagnosis.An example shows that the presented method can reduce attributes effectively with strong fault tolerant ability and the fault of electric power grid can be identified accurately.Compared with rule sets,decision tree is easier to maintain and modify.Furthermore,the decision tree is simple and easy to comprehend.

采用基于粗糙集的决策树方法建立电网故障诊断模型,可以方便地处理含有非数值特征的、不精确的故障样本,且当丢失或出错的故障信息不是关键信号时,不会影响诊断结果,具有较强的容错能力和适应性。该文基于粗糙集理论,首先利用可辨识矩阵的改进算法对由断路器和保护为条件属性、考虑各种故障情况所组成的诊断决策表进行简化;然后采用加权平均粗糙度的概念,作为选择分离属性的标准,构造电网故障决策树,从而实现对电网的故障诊断。通过实例表明,该方法能有效地约简知识,具有很强的容错能力,能准确地判断电网故障以及对其进行定位。与规则表示相比,决策树直观、易于理解、维护和修改。

To improve the indeterminacy and imperfection of electric power fault information that cause the difficulty to obtain accuracy fault diagnosis results, a method is proposed in which by use of improved discernible matrix the fault samples are reduced and the reduced samples are layered according to the value of weighted mean roughness, thereby a fault diagnosis layer model is built. With the proposed method the blindness and redundancy of layering can be avoided and the space for diagnosis model is evidently...

To improve the indeterminacy and imperfection of electric power fault information that cause the difficulty to obtain accuracy fault diagnosis results, a method is proposed in which by use of improved discernible matrix the fault samples are reduced and the reduced samples are layered according to the value of weighted mean roughness, thereby a fault diagnosis layer model is built. With the proposed method the blindness and redundancy of layering can be avoided and the space for diagnosis model is evidently diminished. In addition the weighted mean roughness is easy to be calculated and implemented. According to the value of weighted mean roughness the priority of different characteristic information can be distinguished, so the proposed method possesses strong tolerant ability. Simulation results show that the built model is effective.

针对配电网发生故障后故障诊断警报信息存在不确定性和不完整性导致难以得出准确结论的问题,提出了利用改进可辨识矩阵对故障样本进行约简,在约简的基础上根据加权平均粗糙度的大小对故障样本进行分层从而建立模型的方法。该方法避免了分层的盲目性和冗余性,大大缩小了诊断模型空间,且加权平均粗糙度计算简单,易于实现。按加权平均粗糙集的大小区分层内各特征信息的优先级,具有很强的容错能力。仿真实例证明了该模型的有效性。

 
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