基于Bp神经网络深度学习的供水管网泄漏智能定位

Xiumei Tian
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引用次数: 0

摘要

BP神经网络的使用主要取决于操作者对其的信心。在大多数供水公司中,集成到仿真模型中的水力方程可以自动从地理信息系统(GIS)中生成模型。根据第一次模拟与现有测量结果的比较,需要智能定位。本文介绍了BP神经网络深度学习算法从宏观到微观校正水平的调整范围。在宏观智能定位过程中,以工程师为基础进行分析,并将结论形式化,以供将来在其他网络中使用。微智能定位的关键是通过遗传算法优化设置发射机系数。工作成果包括调整后的决策模型、供水管网渗漏智能定位以及在管网其他部分应用的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Location of Leakage in Water Supply Network Based on Bp Neural Network Deep Learning
The use of BP neural networks depends mainly on operators' confidence in them. In most water supply companies, the hydraulic Eq. integrated into the simulation model can be used to generate models from the geographic information system (GIS) automatically. Upon the comparison of the first simulation with the available measurement results, intelligent location is required. In this paper, the adjustment range of the BP neural network deep learning algorithm from macro to micro correction level is introduced. In the macro intelligent location process, analysis is performed based on engineers, and the conclusions are formalized for future use in other networks. The key point of the micro-intelligent location is to set the transmitter coefficients through genetic algorithm optimization. The work results include an adjusted decision model, intelligent location of leakage in the water supply network, and methods applied to other parts of the network.
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