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Study of Discharge Model in South-to-North Water Diversion Middle Route Project Based on Radial Basis Function Neural Network

机译:基于径向基函数神经网络的南水北调中线工程流量模型研究

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The technology for water dispatch is very complex in South-to-North Water Diversion Middle Route Project, and it is necessary to take advantage of automation system for water delivery. The model for calculating flow rate is important to water dispatch, but traditional method often needs to rectify parameters manually. A model based on radial basis function neural network is established to describe the relationship between water level, gate opening and flux. The model uses the network to simulate the optimal function between water level, gate opening and flux coefficient, and calculates the flow rate by the coefficient. By taking the new method into South-to-North Water Diversion Middle Route Project and comparing the neural network model with traditional methods, the results show that the radial basis function neural network model has higher accuracy and efficiency
机译:南水北调中线工程中的输水技术非常复杂,必须利用自动化系统进行输水。计算流量的模型对于水的分配很重要,但是传统方法经常需要手动校正参数。建立了基于径向基函数神经网络的模型来描述水位,闸门开度和通量之间的关系。该模型使用网络模拟水位,闸门开度和通量系数之间的最佳函数,并根据该系数计算流量。将新方法引入南水北调中线工程,并将神经网络模型与传统方法进行比较,结果表明径向基函数神经网络模型具有较高的精度和效率。

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