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郭文兵, 邓喀中, 邹友峰. 地表下沉系数计算的人工神经网络方法研究[J]. 岩土工程学报, 2003, 25(2): 212-215.
引用本文: 郭文兵, 邓喀中, 邹友峰. 地表下沉系数计算的人工神经网络方法研究[J]. 岩土工程学报, 2003, 25(2): 212-215.
GUO Wenbing, DENG Kazhong, ZOU Youfeng. Study on artificial neural network method for calculation of subsidence coefficient[J]. Chinese Journal of Geotechnical Engineering, 2003, 25(2): 212-215.
Citation: GUO Wenbing, DENG Kazhong, ZOU Youfeng. Study on artificial neural network method for calculation of subsidence coefficient[J]. Chinese Journal of Geotechnical Engineering, 2003, 25(2): 212-215.

地表下沉系数计算的人工神经网络方法研究

Study on artificial neural network method for calculation of subsidence coefficient

  • 摘要: 在综合分析地表下沉系数影响因素的基础上 ,采用人工神经网络方法建立了地表下沉系数的计算模型 ,运用我国典型的地表移动观测站资料作为网络模型的学习训练样本和测试样本 ,对网络模型的计算结果与实测值进行了对比。结果表明 ,用人工神经网络方法求算地表下沉系数考虑的因素更为全面 ,结果准确可靠 ,更接近于实际 ,为地表下沉系数的理论计算探索出一种新方法

     

    Abstract: The main factors influencing subsidence coefficient were comprehensively analyzed. Then the model to calculate subsidence coefficient was established by applying the theory of artificial neural network (ANN). A large amount of data of observation stations was used as learning and training samples to train and test the artificial neural network model. The calculated results of the ANN model and the observed values were compared and analyzed. The results show that it is comparatively precise to calculate the subsidence coefficient of ground surface by ANN technology.

     

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