卧倒门承船厢内水面最大升降值神经网络计算模型

Neural network models of maximum fluctuation of water surface in ship lift chamber with tumble gate operating

运用人工神经网络原理,在通过大量学习样本对网络进行训练的基础上,建立了卧倒门启闭时升船机承船厢内水面最大升降值的神经网络计算模型(NNCM—MFWS-SLC-TGO)。根据岩滩升船机和三峡升船机的模型试验资料对网络模型的性能进行了测试。测试结果表明,所建立的神经网络模型可用于卧倒门启闭时承船厢内水面最大升降值的初步预测。通过实测,计算结果与测试数据是基本吻合的。

On the basis of network training, neural network computing models of maximum fluctuation of water surface in ship lift chamber with tumble gate operating (NNCM-MFWS-SLC-TGO) were built through the principle of artificial neural networks. The characteristics of the models were tested according to the experiment data from Yantan Ship Lift and Three Gorges Ship Lift. And the result indicates that the models may be applied to forecast maximum water level fluctuation in ship lift chamber with tumble gate operating. Furthermore, a new way and method about the study on ship lift chamber hydrodynamics were provided.