| 基于灰色最小二乘支持向量机的边坡位移预测 |
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| 引用本文： | 马文涛.基于灰色最小二乘支持向量机的边坡位移预测[J].岩土力学,2010,31(5):1670-1674. |
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| 作者姓名： | 马文涛 |
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| 作者单位： | 宁夏大学,数学计算机学院,银川,750021 |
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| 基金项目： | 宁夏自然科学基金资助项目，宁夏大学自然科学基金资助项目 |
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| 摘 要： | 利用边坡实测位移序列预测边坡未来时间的位移，可以有效地判断边坡的稳定性。在分析了灰色预测方法和最小二乘支持向量机各自的优缺点的基础上，提出了将二者相结合的一种新的预测模型——灰色最小二乘支持向量机预测模型。新模型既发挥了灰色预测方法中“累加生成”的优点，弱化了原始序列中随机扰动因素的影响，增强了数据的规律性，又充分利用了最小二乘支持向量机求解速度快、易于描述非线性关系的优良特性，避免了灰色预测方法及模型存在的理论缺陷。同时，采用遗传算法进行了模型的参数优化，通过2个工程实例说明灰色最小二乘支持向量机模型预测边坡位移的有效性，具有较高的精度。
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| 关 键 词： | 边坡位移 灰色模型 最小二乘支持向量机 遗传算法 时间序列 |
| 收稿时间： | 2008-11-07 |
Forecasting slope displacements based on grey least square support vector machines |
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| MA Wen-tao.Forecasting slope displacements based on grey least square support vector machines[J].Rock and Soil Mechanics,2010,31(5):1670-1674. |
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| Authors: | MA Wen-tao |
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| Institution: | College of Mathematics & Computer Engineering, Ningxia University, Yinchuan 750021, China |
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| Abstract: | Based on the displacement sequence of slope, the stability of slope could be judged effectively by forecasting the displacement of slope in the future. Through analyzing advantages and disadvantages of grey forecasting methods and least square support vector machines(LSSVM) respectively , a new forecasting model of grey least square support vector machine was proposed. The new model not only developed the advantages of accumulation generation of the grey forecasting method, weakened the effect of stochastic-disturbing factors in original sequence and strengthened the regularity of data, but also used the quickly solving speed and the excellent characteristics of least square support vector machines for nonlinear relationship and avoided the theoretical defects existing in the grey forecasting model. At the same time, the genetic algorithms were used to optimize the parameters of new model. At last, two engineering examples are given to testify the effectiveness of the grey least square support vector machine method to forecast displacements of slope; the results show that the new model has higher precision. |
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| Keywords: | slope displacement grey model least square support vector machines genetic algorithms time sequence |
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