Earticle

초록

영어

Due to the normal forecasting methods for subgrade settlement using observation data have different applicabilities, and the predicting results has bigger volatility and lower accuracy. In view of the above problems, a method based on least square support vector regression (LSTSVR) and real-coded quantum evolutionary algorithm (RQEA) is proposed. Firstly, the LSTSVR parameter is chosen as a combinatorial optimization problem, and determining the objective function of the combinatorial optimization problem, then, using RQEA to solve the combinatorial optimization problem and optimize the LSTSVR parameters, Finaly, LSTSVR-RQEA is used to sovle the prediction of subgrade settlement. The simulation results show that RQEA is an effective method to select LSTSVR parameters, and has excellent performance when applied to the prediction of subgrade settlement.

목차

Abstract
 1. Introduction
 2. Least Square Twin Support Vector Regession
 3. Real Number Encoding Quantum Evolutionary Algorithm
 4. Optimizing LSTSVR Parameters based on RQEA
 5. Engineering Example
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• GAO Hui [ School of Electrical and Information Engineering, Heilongjiang institute of Technology, Harbin, Heilongjiang, China ]
• SONG Qi-chao [ School of Electrical and Information Engineering, Heilongjiang institute of Technology, Harbin, Heilongjiang, China ]
• Huang Jun [ Faculty of Geosciences and Environmental Engineering, Southwest Jiantong University, Chengdu, Sichuan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605