《Journal of Hydroinformatics》刊登“对于判断U形水槽水跃长度的ANN法的评估”

作者：Houichi Larbi1,Dechemi Noureddine,Heddam Salim,Achour Bachir

刊物：《水信息学杂志》，2013年，第15卷第1期，147-154页

关键词：人工神经网络，广义回归神经网络，水跃长度，多层感知器神经网络，多重回归

摘要：利用理论和经验模型对特征水跃进行建模已经成为一项艰巨的任务。水跃长度被定义为水跃尖到其表面上升的位置之间的距离。由于存在高速扰动，水跃长度无法通过理论简单的确定。但是，为了设计消力池要对其进行试验研究。这项任务首先经过理论和试验验证取消了使用宽顶槛来控制U形槽水跃，通过多元回归（MR）分析开始研究。为了建立水跃相对长度的模型，应用并评估了两个不同的人工神经网络（ANN），即多层感知器神经网络（MLPNN）和广义回归神经网络（GRNN）。结果表明，GRNN对预测自适应传输值的水力问题具有一定的预测强度和可能性。然而，利用MLPNN模型仍可以对这些表现出的性能指标做出最好的分类。

An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel

Authors: Houichi Larbi1,Dechemi Noureddine,Heddam Salim,Achour Bachir

Journal: Journal of Hydroinformatics. Jan2013, Vol. 15 Issue 1, p147-154. 8p.

Key word: artificial neural network, GRNN, lengths of hydraulic jump, MLPNN, MR

Abstract: Modeling of hydraulic characteristics of jump using theoretical and empirical models has always been a difficult task. The length of jump may be defined as the distance measured from the toe of the jump to the location of the surface rise. Due to high turbulence this length cannot be determined easily by theory. However, it has been investigated experimentally so as to design the stilling basins with hydraulic jumps. In this work, the control of a hydraulic jump by broad-crested sills in a U-shaped channel is recalled theoretically and experimentally examined. The study begins with a multiple regression (MR) analysis. Then, and in order to model the relative lengths of hydraulic jumps, we have implemented and evaluated two different artificial neural networks (ANN): multilayer perception neural network (MLPNN) and generalized regression neural network (GRNN). The results demonstrate the predictive strength of GRNN and its potential to predict hydraulic problems with an adaptive spread value. However, the MLPNN model remains best classified by these indexes of performance.

原文链接：https://vpn2.nlc.gov.cn/prx/000/http/web.ebscohost.com/ehost/detail?vid=5&sid=0f62ed8f-8d7f-4c4c-807e-d4bbd40cb24d%40sessionmgr14&hid=19&bdata=Jmxhbmc9emgtY24mc2l0ZT1laG9zdC1saXZl#db=a9h&AN=84453268