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Diagnosis of the industrial systems by fuzzy classification

机译:模糊分类诊断工业系统

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The aim of this paper is to present a classifier based on a fuzzy inference system, the final decision being made with the help of a thresholder. For this classifier, a parameterization method which is not necessarily based on an iterative training is presented. This approach can be seen as a pre-parameterization of the classifier which allows the building up of a base set of rules, and initialise the parameters of membership function. A continuous and derivable version of the previous classifier is also presented. For this last classifier an iterative learning algorithm based on a gradient method is suggested. An example using the learning basis IRIS, which is a benchmark for the problem of classification, is presneted. This example allows us to compare the performance of this classifier with the works of other authors. Finally this classifier is applied to the diagnosis of a D.C. motor showing the utility of this method.
机译:本文的目的是呈现基于模糊推理系统的分类器,该分类器是在阈值的帮助下进行的最终决策。对于该分类器,呈现了不必基于迭代训练的参数化方法。这种方法可以被视为分类器的预参数化,其允许构建基本规则集,并初始化成员函数的参数。还呈现了先前分类器的连续和可达到的版本。对于此最后分类器,提出了一种基于梯度方法的迭代学习算法。预先使用使用学习基础虹膜的示例,这是分类问题的基准。此示例允许我们将此分类器的性能与其他作者的作品进行比较。最后,该分类器应用于D.C.电机的诊断,显示该方法的效用。

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