本文已被：浏览 1660次 下载 2602次
Received:May 13, 2011 Revised:June 25, 2011
Received:May 13, 2011 Revised:June 25, 2011
中文摘要: 在综合研究了各种算法的基础上,将粗集理论和最小二乘支持向量机算法结合,充分利用了粗集算法能够去除冗余信息,最小二乘支持向量机能够精确加快收敛速的优点。利用具体网络建立一个突出预测机制,并利用本预测机制对矿井瓦斯突出情况进行模拟预测。经过基于MATLAB工具箱的BP神经网络模型的实验对比表明,LS-SVR能加快收敛速度。实验结果表明,基于RS-LS-SVR网络的预测模型可靠,收敛速度快,预测精度高,效果良好。
中文关键词: 煤与瓦斯突出 粗糙集 最小二乘支持向量回归机 模拟预测
Abstract:Based on the comprehensive study of the various algorithms, with the rough set theory and the Least Squares Support Vector Regression, taking advantage of rough set method can remove redundant information, Least Squares Support Vector Regression can accurately accelerate the convergence speed advantages. Prominent use of a specific network prediction mechanism, and use this prediction of mine gas outburst mechanism to predict the situation. After based on the MATLAB neural network toolbox BP neural network method of experimental comparison shows that the LS-SVR can speed up the convergence rate. The experimental result reveals that Based on RS-LS-SVR neural network prediction model is reliable, fast convergence and high accuracy, good effect.
文章编号： 中图分类号： 文献标志码：
基金项目:国家自然科学基金(50874059)
引用文本：
彭泓,高攀.RS-LS-SVR 模型在煤与瓦斯突出预测的应用.计算机系统应用,2012,21(1):65-68,35
PENG Hong,GAO Pan.RS-LS-SVR Model in Predication of the Coal and Gas Outburst.COMPUTER SYSTEMS APPLICATIONS,2012,21(1):65-68,35
彭泓,高攀.RS-LS-SVR 模型在煤与瓦斯突出预测的应用.计算机系统应用,2012,21(1):65-68,35
PENG Hong,GAO Pan.RS-LS-SVR Model in Predication of the Coal and Gas Outburst.COMPUTER SYSTEMS APPLICATIONS,2012,21(1):65-68,35
