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基于神经网络的滑坡变形预测报研究

利用ＢＰ神经网络方法建立了滑坡变形预报模型,在此模型的基础上对几个典型滑坡进行了预报分析,其结果表明用ＢＰ模型进行滑坡短期预报效果较好。     

神经网络在工程场地稳定性评价中的应用

本文应用ＢＰ网络对厦门海沧开发区工程场地的稳定性进行了综合评价,其评价结果与实际情况吻合一致。为工程场地地质质量评估提供了科学依据,同时也表明人工神经网络是解决此类问题的理想方法之一。     

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Inversion of 3D density interface with PSO-BP method

BP( Back Propagation) neural network and PSO( Particle Swarm Optimization) are two main heuristic optimization methods,and are usually used as nonlinear inversion methods in geophysics. The authors applied BP neural network and BP neural network optimized with PSO into the inversion of 3D density interface respectively,and a comparison was drawn to demonstrate the inversion results. To start with,a synthetic density interface model was created and we used the proceeding inversion methods to test their effectiveness. And then two methods were applied into the inversion of the depth of Moho interface. According to the results,it is clear to find that the application effect of PSO-BP is better than that of BP network. The BP network structures used in both synthetic and field data are consistent in order to obtain preferable inversion results. The applications in synthetic and field tests demonstrate that PSO-BP is a fast and effective method in the inversion of 3D density interface and the optimization effect is evident compared with BP neural network merely,and thus,this method has practical value.     

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