库岸高边坡稳定度非线性判估研究

库岸高边坡变形量分析是判估其稳定性的关键因素。但由于库岸高边坡变形受众多因素影响,且各因素之间存在强烈的非线形关系,故难以进行有效的预判。本文提出了基于混沌神经网络模型的方法,对高边坡变形随时间变化的位移量进行了仿真计算,结果表明,此方法高效可行,计算精度高,能够满足工程及控制的要求。

Non-Linear Displacement Chaotic Neural Network Prediction on High=Slope Deformation

The displacement of high-slope is essential to analyzing the stability. However, the displacement of high-slope is influenced by so many factors, and there are non-linear functions between those factors, so it is difficult to have it accurately estimated. This article presents a method of chaotic neural networks to simulate the displacement of high-slope by way of time series. The result indicates that this method is efficient, feasible, and fitful to satisfy the demands in engineering controls.