摘要
人工神经网络具有较强的非线性映射能力和学习能力,通过改进神经网络的BP算法,提高了BP算法的学习收敛速度和网络性能的稳定性。基于改进的BP神经网络模型,建立了可依据现场量测信息对软基路堤沉降量随时间而发展的过程进行动态预报的分析方法。此方法利用实测资料直接建模,避免了传统方法计算过程中各种人为因素的干扰,所建立的模型预测精度高、预测的沉降量误差小。
Some improved steps for the BP neural networks are introduced. The improved steps can increase the convergence speed of BP neural networks, and can improve the performance of BP neural networks. On the basis of the improvement of BP neural network and field measuring information, the dynamic analysis method for predicting the highway settlement which varies with time is established. Since the model of this method is directly based on real samples, it can avoid the mistakes due to factitiousness in traditional methods. It is proved that the prediction model is accurate and the settlement has least error.
出处
《科学技术与工程》
2008年第13期3539-3543,共5页
Science Technology and Engineering
