基于小波网络的三峡永久船闸中隔墩稳定性分析和预测模型

ANALYSIS AND FORECAST ON DIVISION PIER STABILITY OF PERMANENT SHIPLOCK OF THREE GORGES PROJECT WITH WAVELET NETWORK

三峡永久船闸中隔墩的稳定性影响着船闸的安全运营,但其安全系数与控制因素之间具有很强的非线性映射关系。引入小波网络,利用其良好的时频局域化性质和强自学习功能,通过一些工程实例作为网络的训练模式,来刻划和模拟它们之间的非线性映射关系,并将训练好的网络来分析和预测永久船闸中隔墩的稳定性。分析结果表明,模型的拟合和预测精度均较高,可用于定量评价其稳定性;并且,模型具有强抗噪音能力,而这对解决实际工程问题是非常重要的。同时,设计的网络学习算法性能较优,这是由于在小波网络整个参数空间中的子空间能最小化误差目标函数。

The stability of division pier influences the safety of the permanent shiplock of Three Gorges project. The safety coefficient is usually used to evaluate the stability of division pier. Wavelet network combines the time-frequency domain localization properties of wavelet transform and self-learning ability of traditional feed-forward neural network. Wavelet network was used to approximate the nonlinear relation between safety coefficient and control factors of the division pier stability of the permanent shiplock. The stability of division pier can be analyzed and forecasted with wavelet network trained by engineering case as learning samples. The numerical example shows that the precision of approximation and prediction of the proposed model can satisfy the quantitative evaluation for the stability of division pier of the permanent shiplock. The model is of good capability of noise-resistance. The wavelet network is trained by the hydride learning algorithm of Levenbery-Marquardt algorithm and Least-Squares algorithm. Levenbery-Marquardt algorithm can train the nonlinear parameters, Least-Squares algorithm can train the linear parameters. Error objective function is minimized in subspace of whole parameter space of wavelet network with the presented algorithm. Wavelet network is obviously superior to conventional algorithms.