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首页> 外文期刊>IEEE transactions on industrial informatics >Application of Fuzzy Decision Tree for Signal Classification
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Application of Fuzzy Decision Tree for Signal Classification

机译：模糊决策树在信号分类中的应用

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摘要

A typical algorithm for signal classification consists of two steps: signal preliminary transformation and classification itself. The procedures of preliminary transformation are used to extract specific features of the initial signal and reduce its dimension for effective classification. The result of this transformation is information loss of initial signal, which implies uncertainty of data used in classification. This uncertainty can be taken into account by the application of fuzzy classifiers. In this paper, a new algorithm with application of fuzzy classifier is proposed for signal classification. A new procedure of fuzzification is added into the preliminary transformation and fuzzy decision tree is used for classification. The efficiency of this algorithm is examined based on the problem of detection of defective blades of an aircraft engine gas turbine. The experiments showed that the accuracy of the classification for the considered example is 0.989. This is the best result in comparison with other classification methods used to solve this problem.
机译：典型的信号分类算法包括两个步骤：信号初步转换和分类本身。初步变换的过程用于提取初始信号的特定特征，并减小其尺寸以进行有效分类。这种转换的结果是初始信号的信息丢失，这意味着分类中使用的数据的不确定性。可以通过应用模糊分类器来考虑这种不确定性。提出了一种应用模糊分类器的信号分类新算法。将模糊化的新过程添加到初步转换中，并使用模糊决策树进行分类。基于检测飞机发动机燃气轮机的有缺陷叶片的问题来检查该算法的效率。实验表明，所考虑示例的分类准确性为0.989。与用于解决此问题的其他分类方法相比，这是最好的结果。

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