【摘要】 建立安全监测网络模型来分析和预测大坝变形位移信息,对保障大坝安全稳定服役意义重大。针对大坝安全监测BP神经网络模型运算复杂、收敛速度慢、易陷于局部最优、不能准确反映和预测大坝运行状况的问题,引入蚁群算法(ACO)全局搜索功能搜寻BP神经网络参数最优解,并通过样本数据训练BP网络获得大坝变形位移预测值。工程实例应用表明:ACO-BP网络模型在参数优化方面较BP网络更易于收敛,误差较小、预测性能良好,可为大坝变形位移监测和安全预报提供一种新的非线性建模仿真分析方法。更多还原

【Abstract】 Establishing a safety monitoring model to analyze and forecast the deformation and displacement of the dam is of great significance for its safe operation. The monitoring model of BP neural networks has the disadvantages of complicated operation and slow convergence,and it is easy to fall into localized optimazition. These drawbacks will lead to inaccurate expression and prediction of the dam operation situation. In order to solve these problems,the ant colony optimization( ACO) algorithm was introduced to BP networks to search for optimal solution of parameters at overall situation,as well as to obtain the dam deformation prediction data through training the sample data by BP neural networks. The engineering case study indicates that the ACO-BP network model is easier to converge in parameter optimization than that of the BP network with less errors and good prediction performance,which can provide a new approach of nonlinear modeling and simulation for dam deformation displacement monitoring and safety prediction.更多还原