摘要
本文将水动力学模型与遗传神经网络方法结合,对深圳湾生态敏感点潮流的实时变化特性进行了预测。利用人工神经网络得出的模拟结果与经过实测资料验证的海湾二维潮流模型的模拟结果十分吻合,从而说明了将遗传神经网络用于二维潮流运动特征模拟的可行性。
A hybrid approach combining the 2D hydrodynamic model for tidal flow with genetic algorithmbased artificial neural networks(GAANN) is presented.The sitespecific knowledge and numerical results from the hydrodynamic model for several typical tidal patterns can be encapsulated in an artificial neural network and taken as the basis of the training in ANNs,which can significantly enhance the simulation speed.A case study is carried out for the real time process prediction of tidal characteristics in Deep Bay,Southern China.The GAANN functioned as nonlinear dynamic system effectively reproduces the behaviors of the tides in the Bay for any given open boundary condition at the bay mouth.The verification results of GAANN are acceptable as compared with the results of numerical models.
出处
《水利学报》
EI
CSCD
北大核心
2003年第10期87-95,共9页
Journal of Hydraulic Engineering
基金
国家自然科学基金委员会和水利部联合资助项目(59890200)