A predicative study of loosening zones around roadways with least square support vector machines method with optimized parameters

MA Wen-tao

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

A predicative study of loosening zones around roadways with least square support vector machines method with optimized parameters

更新时间：2026-06-25

- A predicative study of loosening zones around roadways with least square support vector machines method with optimized parameters
- Vol. 28, Issue S1, (2007)
- 作者机构：
  
  宁夏大学数学计算机学院,银川,750021
- 作者简介：
- 基金信息：
- DOI：
  CLC： Tv223.21
- Online First：28 March 2014，
  
  Published：2007
- 稿件说明：
移动端阅览
MA Wen-tao. A predicative study of loosening zones around roadways with least square support vector machines method with optimized parameters[J]. 2007, 28(S1).
DOI：

MA Wen-tao. A predicative study of loosening zones around roadways with least square support vector machines method with optimized parameters[J]. 2007, 28(S1). DOI：

摘要

最小二乘支持向量机方法(LSSVM)在处理小样本、高维数、非线性的问题时，具有求解速度快、易于描述非线性关系的优良特性。但是，该方法得到的模型拟合精度和泛化能力依赖于其相关参数，因此，提出基于粒子群优化算法(PSO)的LSSVM参数优选方法。最后，用该模型对巷道围岩松动圈进行了预测研究。结果表明，PSO优化的LSSVM模型具有收敛速度快、计算精度高的特点，说明该模型是合理、有效的。

Abstract

Least square vector machines had the quickly solving speed and the excellent characteristics to describe the little samples

nonlinear and high dimensions problem. But the regression accuracy and generalization performance of this method depend on a proper setting of its parameters. So

an optimal selection approach of LSSVM was presented based on particle swarm optimization (PSO) algorithm. Then

the prediction on the thickness of the loosen zone around roadway is made by PSO-LSSVM model

and the results show that this model has the characteristic of quickly convergence speed and highly calculating precision .So

this model is reasonable and feasible.

关键词

Keywords

references

Views

455

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Blasting vibration parameters using comprehensive regression of wavelet denoising and particle swarm optimization algorithm

Settlement prediction of tunnel based on multiple kernels learning mode

Prediction of peak particle velocity induced by underwater blasting based on the combination of grey relational analysis and genetic neural network

Simulation of soil-water characteristic curves during drying and wetting cycles

Swelling characteristics of Gaomiaozi bentonite saturated by salt solution and their prediction

Related Author

ZHANG Le-wen

WANG Hong-bo

QUY Dao-hong

SUN Huai-feng

SUN Zi-zheng

DING Wan-tao

FAN Si-xia

ZHOU Qi-cai

Related Institution

Research Center of Geotechnical and Structural Engineering, Shandong University

School of Mechanical and Engineering, Tongji University

State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology

Department of Civil Engineering, Shanghai University

AI问答

⁰