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用于混沌时间序列预测的组合核函数最小二乘支持向量机
  • ISSN号：1000-3290
  • 期刊名称：《物理学报》
  • 时间：0
  • 分类：TP183[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
  • 作者机构：[1]沈阳工业大学信息科学与工程学院,沈阳110870, [2]东北大学信息科学与工程学院,沈阳110819, [3]辽宁林业职业技术学院人文社会科学系,沈阳110101
  • 相关基金：国家自然科学基金重点项目（批准号：61034005）资助的课题.
作者： 田中大[1], 高宪文[2], 石彤[3]
关键词： 混沌时间序列, 最小二乘支持向量机, 组合核函数, 改进遗传算法, chaotic time series, least squares support vector machine, combination kernel function, improved genetic algorithm
中文摘要：

针对混沌时间序列的预测问题，考虑到单一核函数的最小二乘支持向量机无法明显提高预测精度，提出了一种组合核函数的最小二乘支持向量机预测模型，模型中采用多项式函数与径向基函数组合构建核函数。同时，还对遗传算法进行了改进，使之具有更快的收敛速度和更高的精度，改进的遗传算法适用于解决预测模型中的参数优化问题。通过典型的Lorenz时间序列、Mackey-Glass时间序列、太阳黑子数时间序列以及具有混沌特性的网络流量时间序列对该模型进行了验证。仿真结果表明所提出的模型是有效的。

英文摘要：

Considering the problem that least squares support vector machine prediction model with single kernel function cannot significantly improve the prediction accuracy of chaotic time series, a combination kernel function least squares support vector machine prediction model is proposed. The model uses a polynomial function and radial basis function to construct the kernel function of least squares support vector machine. An improved genetic algorithm with better convergence speed and precision is proposed for parameter optimization of prediction model. The simulation experimental results of Lorenz, Mackey-Glass, Sunspot-Runoff in the Yellow River and chaotic network traffic time series demonstrate the effectiveness and characteristics of the proposed model.

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期刊信息
  • 《物理学报》
  • 北大核心期刊（2011版）
  • 主管单位:中国科学院
  • 主办单位:中国物理学会 中国科学院物理研究所
  • 主编：欧阳钟灿
  • 地址：北京603信箱(中国科学院物理研究所)
  • 邮编：100190
  • 邮箱：apsoffice@iphy.ac.cn
  • 电话：010-82649026
  • 国际标准刊号：ISSN：1000-3290
  • 国内统一刊号：ISSN：11-1958/O4
  • 邮发代号:2-425
  • 获奖情况:
  • 1999年首届国家期刊奖,2000年中科院优秀期刊特等奖,2001年科技期刊最高方阵队双高期刊居中国期刊第12位
  • 国内外数据库收录:
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  • 被引量:49876