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ARTIFICIAL NEURAL NETWORKS ENSEMBLE USED FOR PIPELINE LEAK DETECTION SYSTEMS

机译:用于管道泄漏检测系统的人工神经网络集合

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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:管道的物理和运营特性大大变化。因此,没有普遍适用的方法,外部或内部,具有完美泄漏检测性能所需的所有功能和功能。本文的作者非常了解,传统方法在低不确定性环境中,克服了泄漏检测系统的人工智能方法。如果一个人认为现实世界作为不确定因素的创造者,神经网络和模糊系统就会成为泄漏检测系统发展的重要承诺技术。在这项工作中,我们提出了一种制定用于管道泄漏检测的ANN的集合的方法。我们实验中获得的结果令人满意。

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