计算机工程与科学2012，Vol.34Issue(7)：177-181,5.DOI:10.3969/j.issn.1007-130X.2012.07.033
基于粒子群优化支持向量机的煤矿水位预测模型
The Forecast Model of Mine Water Discharge Based on Particle Swarm Optimization and Support Vector Machines
摘要
Abstract
The support vector machine (SVM) algorithm is of reliable global optimality and good generalization,suitable for the learning of finite samples. However, the results considerably depend on the SVM model parameters and the conventional parameter choosing method by experience is unsatisfactory. Using the particle swarm optimization (PSO) random search strategy, we can establish the optimization parameters of support vector machine. It is shown that ACOSVM is much better in the simulation results than the artificial neural network, which greatly improves in fitting precision,and it has good generalization ability.关键词
支持向量机/粒子群优化/参数优选/水仓水位Key words
support vector machine/particle swarm optimization/parameter optimization/water warehouse water level分类
信息技术与安全科学引用本文复制引用
郭凤仪,郭长娜,王爱军,王洋洋,刘丹..基于粒子群优化支持向量机的煤矿水位预测模型[J].计算机工程与科学,2012,34(7):177-181,5.基金项目
辽宁省高校创新团队资助项目(LT2010046) （LT2010046）
