| 基于自适应粒子群优化的SVM算法在建筑物沉降预测中的应用 |
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| 引用本文： | 张潇珑.基于自适应粒子群优化的SVM算法在建筑物沉降预测中的应用[J].测绘工程,2015(11):44-47. |
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| 作者姓名： | 张潇珑 |
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| 作者单位： | 江西信息应用职业技术学院 环境工程系,江西 南昌,330043 |
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| 摘 要： | 针对传统支持向量机算法在预测方面的不足,采用自适应粒子群算法(APSO)对支持向量机参数选择进行分析和优化,建立基于自适应粒子群优化的SVM算法建筑物沉降预测模型,并对建筑物进行沉降预测。实验表明,相比于传统的支持向量机算法,自适应粒子群优化的SVM算法预测精度较高,为建筑物沉降预测提供一种新方法。
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| 关 键 词： | 自适应粒子群 支持向量机 建筑物沉降预测 |
Application of SVM based on adaptive particle swarm optimization algorithm to building settlement prediction |
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| Abstract: | In view of the deficiency of the traditional support vector machine algorithm in forecasting ,the adaptive particle swarm optimization (APSO) is used to analyze and optimize the parameters of support vector machine ,by building the settlement prediction model based on the SVM algorithm for adaptive particle swarm optimization ,and predicting the settlement of buildings .The experimental results show that ,compared with the traditional support vector machine algorithm ,the SVM algorithm for adaptive particle swarm optimization is more accurate in prediction ,to provide a new method for predicting the settlement of buildings . |
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| Keywords: | APSO support vector machines building subsidence prediction |
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