人工神经网络压力估计器的最优结构

IF 0.9 Q4 WATER RESOURCES
Rui Gabriel Souza, B. Brentan, G. Lima
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引用次数: 1

摘要

摘要:了解配水管网的水力参数可以实时发现问题,如管道爆裂、小泄漏、管道粗糙度增加和非法连接等。然而,准确的指示取决于所获得数据的数量和质量,即用于监测网络的传感器的数量及其位置。拥有大量的传感器在经济上是不可行的,因此,利用人工智能,如人工神经网络(ann)可以减少识别问题所需信息的缺乏,通过收集到的少量信息来估计水力参数。人工神经网络的可靠性取决于它的结构,因此,本文通过测试不同的条件来训练人工神经网络,以确定当人工神经网络用于压力估计时,哪些是需要调整的最相关参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal architecture for artificial neural networks as pressure estimator
ABSTRACT The knowledge of hydraulic parameters in water distribution networks can indicate problems in real time, such as pipe bursts, small leakages, increase in pipe roughness and illegal connections. However, an accurate indication relies on the quantity and quality of the data acquired, i.e., the number of sensors used to monitor the network and their location. It is not economic feasible have a great number of sensors, thus, the use of artificial intelligence, such as Artificial Neural Networks (ANNs) can reduce the lack of information necessary to identify problems, estimating hydraulic parameter through the few information collected. The reliability of ANNs depends on its architecture, so, in this paper, different conditions are tested for ANN training to identify which are the most relevant parameters to be adjusted when the ANN is used for pressure estimation.
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来源期刊
CiteScore
1.60
自引率
12.50%
发文量
18
审稿时长
16 weeks
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