首页> 外文会议>Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE >Combining neural networks, fuzzy logic, and Kalman filtering in an oil leak detector for underground electric power cables
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Combining neural networks, fuzzy logic, and Kalman filtering in an oil leak detector for underground electric power cables

机译:将神经网络,模糊逻辑和卡尔曼滤波结合在地下电力电缆的漏油检测器中

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摘要

This paper presents some of the issues that must be dealt with during the implementation of an oil leak detector in underground power cables. By using a very limited number of sensors, the detector must perform a considerable amount of signal processing in order to achieve reasonable security and dependability. Three original solutions making use of Neural Network, Fuzzy Logic, and Kalman Filtering are presented and compared.
机译:本文介绍了在地下电力电缆中安装漏油检测器时必须解决的一些问题。通过使用数量非常有限的传感器,检测器必须执行大量的信号处理,以实现合理的安全性和可靠性。提出并比较了使用神经网络,模糊逻辑和卡尔曼滤波的三种原始解决方案。

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