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ROBUST FAULT DETECTION USING NEURO-FUZZY NETWORKS

机译:基于神经网络的鲁棒故障检测

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摘要

The paper focuses on the problem of robust fault detection using neuro- fuzzy model based strategies. The main objective of the work is to show how to employ bounding error approach to determine the uncertainty of the neuro- fuzzy model and next utilize this knowledge for robust fault detection. The paper presents also how to tackle the problem of choosing the right structure of the neuro- fuzzy models. Proposed algorithms are applied to fault detection in the valve that is the part of the technical installation at the Lublin sugar factory. Experimental results presented in the nal part of the paper conrms the efdctiveness of the proposed methods.
机译:本文着重研究基于神经模糊模型的策略的鲁棒故障检测问题。这项工作的主要目的是说明如何使用边界误差方法来确定神经模糊模型的不确定性,然后如何利用该知识进行鲁棒的故障检测。本文还介绍了如何解决选择神经模糊模型的正确结构的问题。提议的算法应用于阀门的故障检测,这是鲁布林制糖厂技术安装的一部分。本文最后部分给出的实验结果证实了所提方法的有效性。

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