CAJ | 학술논문

针对单一模型在隧道变形预测上精度不高的问题，提出了一种基于小波分析理论的神经网络模型，该模型克服了 BP神经网络模型存在的收敛速度慢、结构设计盲目、易陷入局部极小点的缺陷，通过将该模型与时间序列模型、Levenberg-Marquardt法 BP神经网络模型、遗传神经网络模型预测的结果比较，可以看出小波神经网络在隧道的变形预测中网络结构更简单、收敛速度更快、预测精度更高。
침대단일모형재수도변형예측상정도불고적문제，제출료일충기우소파분석이론적신경망락모형，해모형극복료 BP신경망락모형존재적수렴속도만、결구설계맹목、역함입국부겁소점적결함，통과장해모형여시간서렬모형、Levenberg-Marquardt법 BP신경망락모형、유전신경망락모형예측적결과비교，가이간출소파신경망락재수도적변형예측중망락결구경간단、수렴속도경쾌、예측정도경고。
Based on the fact that single model’s prediction precision in tunnel deformation monito-ring is not very high,this paper proposes a Neural Network model based on Wavelet analysis theory, which can overcome slow convergence speed,blindness of structure design and local minimum of BP Neural network.Compared with time series model,Levenberg-Marquardt BP Neural Network model,and GA-BP Neural Network model,prediction results show that Wavelet Neural Network model has a simple network structure,faster convergence speed and higher prediction precision in tunnel deformation monito-ring.