基于粒子群优化支持向量机的煤矿水位预测模型

J4 ›› 2012, Vol. 34 ›› Issue (7): 177-181.

基于粒子群优化支持向量机的煤矿水位预测模型

郭凤仪1,郭长娜1, 王爱军2，王洋洋1，刘丹1

(1.辽宁工程技术大学电气与控制工程，葫芦岛125105;2.开滦钱家营矿业公司，河北唐山 063301)

出版日期:2012-07-25 发布日期:2012-07-15

The Forecast Model of Mine Water Discharge Based on Particle Swarm Optimization and Support Vector Machines

GUO Fengyi1,GUO Changna1,WANG Aijun2,WANG Yangyang1,LIU Dan1

(1.School of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105;2.Kailuan Qianjiaying Mine Company，Tangshan 063301,China)

Online:2012-07-25 Published:2012-07-15

摘要/Abstract

摘要：

支持向量机算法（SVM) 具有可靠的全局最优性和良好的泛化能力，适用于有限样本的学习，而该算法的成功与否很大程度上取决于其参数的选择，而常规经验选取方法往往不能获得满意效果。利用粒子群算法（PSO）随机搜索策略对支持向量机参数进行优选，建立基于粒子群算法参数优化的支持向量机模型(PSOSVM) 。仿真结果表明，该优化模型比传统的人工神经网络（BP）模拟效果要好，在拟合精度方面有很大的提高，且具有较好的泛化能力。

关键词: 支持向量机；粒子群优化；参数优选；水仓水位

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 ACOSVM is much better in the simulation results than the artificial neural network，which greatly improves in fitting precision，and it has good generalization ability.

Key words: support vector machine;particle swarm optimization;parameter optimization;water warehouse water level

郭凤仪1,郭长娜1, 王爱军2，王洋洋1，刘丹1. 基于粒子群优化支持向量机的煤矿水位预测模型[J]. J4, 2012, 34(7): 177-181.

GUO Fengyi1,GUO Changna1,WANG Aijun2,WANG Yangyang1,LIU Dan1. The Forecast Model of Mine Water Discharge Based on Particle Swarm Optimization and Support Vector Machines[J]. J4, 2012, 34(7): 177-181.