基于RBF神经网络的堤防管涌预测方法

Prediction of Piping Occurring in Embankment Based on RBF Neural Network

摘要:

影响堤防管涌的各种因素是不确定和随机的,在堤防管涌的发生过程中各因素间表现出复杂的非线性行为。运用基于RBF神经网络的基本原理,建立了堤防管涌预测的RBF神经网络模型,从新的角度研究堤防管涌的预测问题,对该问题进行了探索性的研究。对该理论的建立以及预测方法进行了系统的讨论,为该领域的研究提供了完整的技术方法。对于23个典型堤防管涌实例的研究表明,RBF网络较BP网络有较高的预测精度,较短的预测时间和较快的预测速度,能够较好地描述堤防管涌的非线性特征。

Abstract:

The factors that influence the piping occurring in embankment are uncertain and random, and in the process of piping they turn out to be nonlinear behavior. RBF neural network theory was used to establish a new model in order to predict the piping of embankment. This paper discussed the establishment of predicting model and process of application in detail. Comparing with BP neural network, the application in 23 cases of embankment piping shows that RBF network be more precise, more quickly and faster.