| 基于RBF神经网络的边坡稳定性预测 |
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| 引用本文： | 徐进,彭雄志,欧加加,胡伟明.基于RBF神经网络的边坡稳定性预测[J].四川建筑科学研究,2012(1):130-133. |
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| 作者姓名： | 徐进 彭雄志 欧加加 胡伟明 |
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| 作者单位： | 西南交通大学土木工程学院,四川成都610031 |
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| 基金项目： | 西南交通大学大学生科研训练计划（SRTP）专项基金资助项目（100102） |
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| 摘 要： | RBF神经网络具有极强的非线性映射能力,精度高。本文基于RBF神经网络原理,采用自组织选取中心法,建立基于RBF神经网络的边坡稳定性预测模型,并选取大量边坡工程数据作为学习训练和预测样本,利用该模型进行学习和预测。研究结果表明,在处理边坡稳定性预测问题中,该方法具有很好的适应性和较高的精度。
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| 关 键 词： | 边坡稳定 RBF神经网络 自组织选取中心算法 |
A prediction of the slope stability based on RBF neural network |
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| XU Jin,PENG Xiongzhi,OU Jiajia,HU Weiming.A prediction of the slope stability based on RBF neural network[J].Building Science Research of Sichuan,2012(1):130-133. |
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| Authors: | XU Jin PENG Xiongzhi OU Jiajia HU Weiming |
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| Affiliation: | ( School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China) |
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| Abstract: | RBF neural network has very strong nonlinear mapping ability and high accuracy. In this paper, a predicting model of slope stability has been established based on the RBF neural network theories using self-organizing selection center algorithm. Then a large number of engineering data of slope are selected as samples to train and forecast. The study suggests that the method has good adaptability and high precision in dealing with the problem of slope stability prediction. |
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| Keywords: | slope stability RBF neural network self-organizing selection center algorithm |
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