A Fault Diagnosis Modeling Method Combined RBF Neural Network with Rough Set Theory
首发时间:2009-06-25
Abstract:In order to improve diagnosis precision and decreasing misinformation diagnosis, according to the intelligence complementary strategy, a new complex intelligent fault diagnosis method based on rough sets theory and RBF neural network is presented. Firstly, basis on data pretreatment, the fault diagnosis decision table is formed, and continuous datum are discretized by using hybrid clustering method. Rough sets theory as a new mat hematical tool is used to deal with inexact and uncertain knowledge for pattern recognition. The target is mainly to remove redundant information and seek for reduced decision tables which to obtain the minimum fault feature subset. The neural networks adopted were of the feed-forward variety with one hidden layer. They were trained using back-propagation. The method can reduce the false alarm rate and missing alarm rate of the fault diagnosis system effectively, and can detect the composed faults while keep good robustness.
keywords: fault diagnosis modeling rough set theory RBF Neural Network discretization
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基于神经网络和粗糙集的故障诊断模型
摘要:为了提高诊断精度和减少错误的诊断,提出了一种基于粗糙集理论和RBF神经网络的故障诊断模型。首先,对数据进行预处理,形成故障诊断判定表,对连续的数据通过混合聚类方法离散化。粗糙集理论是一种新的用于模式识别中处理不精确、不确定知识的数学工具,主要目标是去除多余的信息,寻求减少的判定表以获得最少错误的特征子集。该神经网络隐含层采用前馈方式,用的训练方式是BP(反向传播)该方法能减少故障诊断系统的灵敏度和失误率,有很强的鲁棒性来侦察组合错误。
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No.3340532053812458****
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