摘要
根据丰满大坝多年变形观测数据,建立了基于进化神经网络混凝土大坝变形预测方法。经典的BP神经网络的缺陷在于收敛速度慢和泛化能力弱等特性。与普通的多元回归方法和传统的BP神经网络相比,采用遗传算法训练的人工神经网络预测模型预报大坝的变形具有精度高和全局收敛的特点。在丰满大坝工程实际应用表明,所建立的基于进化神经网络混凝土大坝变形预报方法与广泛采用的统计方法相比,可以显著提高大坝变形预报精度。
Based on the measured data of Fengman dam deformations for many years, the artificial neural network evolved by a genetic algorithm was adopted for forecasting the dam deformation. The shortcomings of the traditional BP artificial neural network lie in the slowness in the convergence rate and the weakness in the generalization ability. Compared with the popular multi-factor regression model and BP artificial neural network, the forecasting model based on artificial neural network evolved by a genetic algorithm had the characteristics of accurately forecasting and global convergence. The practical application to Fengman concrete dam shows that, compared with a commonly used statistical method, the forecasting method proposed can obviously enhance the forecasting precision of dam deformation.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2003年第4期634-638,共5页
Rock and Soil Mechanics
基金
国家自然科学基金资助项目(批准号:10072014)
高校博士点基金资助项目。
