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ARTIFICIAL NEURAL NETWORKS ENSEMBLE USED FOR PIPELINE LEAK DETECTION SYSTEMS

机译:可用于管道泄漏检测系统的人工神经网络

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The physical and operational properties of pipelines vary greatly. There is thus no universally applicable method, external or internal, which possesses all the features and the functionality required for a perfect leak detection performance. The authors of this paper know quite well that traditional methods, in a low uncertainty environment, overcome artificial intelligence methods of leak detection systems. If one considers the real world as a creator of uncertainties, neural networks and fuzzy systems emerge as important promising technologies for the development of leak detection systems. In this work, we propose a method for constructing ensembles of ANNs for pipeline leak detection. The results obtained in our experiments were satisfactory.
机译:管道的物理和操作属性差异很大。因此,没有一种普遍适用的外部或内部方法,该方法具有完美泄漏检测性能所需的所有功能。本文的作者非常清楚,在不确定性较低的环境中,传统方法克服了泄漏检测系统的人工智能方法。如果人们将现实世界视为不确定性的创造者,那么神经网络和模糊系统将成为开发泄漏检测系统的重要有希望的技术。在这项工作中,我们提出了一种用于管道泄漏检测的人工神经网络的集成方法。在我们的实验中获得的结果是令人满意的。

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