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基于支持向量机回归的T-S模糊模型自组织算法及应用

梁炎明 苏芳 李琦 刘丁

梁炎明, 苏芳, 李琦, 刘丁. 基于支持向量机回归的T-S模糊模型自组织算法及应用. 自动化学报, 2013, 39(12): 2143-2149. doi: 10.3724/SP.J.1004.2013.02143
引用本文: 梁炎明, 苏芳, 李琦, 刘丁. 基于支持向量机回归的T-S模糊模型自组织算法及应用. 自动化学报, 2013, 39(12): 2143-2149. doi: 10.3724/SP.J.1004.2013.02143
LIANG Yan-Ming, SU Fang, LI Qi, LIU Ding. A Self-organizing Algorithm for T-S Fuzzy Model Based on Support Vector Machine Regression and Its Application. ACTA AUTOMATICA SINICA, 2013, 39(12): 2143-2149. doi: 10.3724/SP.J.1004.2013.02143
Citation: LIANG Yan-Ming, SU Fang, LI Qi, LIU Ding. A Self-organizing Algorithm for T-S Fuzzy Model Based on Support Vector Machine Regression and Its Application. ACTA AUTOMATICA SINICA, 2013, 39(12): 2143-2149. doi: 10.3724/SP.J.1004.2013.02143

基于支持向量机回归的T-S模糊模型自组织算法及应用

doi: 10.3724/SP.J.1004.2013.02143
基金项目: 

国家自然科学基金(61203114),陕西省自然科学基金(2013JM8029)资助

详细信息
    作者简介:

    梁炎明 西安理工大学自动化与信息工程学院副教授,博士研究生. 主要研究方向为复杂系统建模与控制. 本文通信作者.E-mail:liangym@xaut.edu.cn

A Self-organizing Algorithm for T-S Fuzzy Model Based on Support Vector Machine Regression and Its Application

Funds: 

Supported by National Natural Science Foundation of China (61203114), and Natural Science Foundation of Shaanxi Province (2013JM8029)

  • 摘要: 结合模糊聚类算法和支持向量机回归算法提出了一种新的T-S模糊模型自组织算法. 该算法首先利用一种改进模糊聚类算法提取模糊规则和辨识前件参数,然后将T-S模糊模型后件变换为标准线性支持向量机回归模型,并利用支持向量机回归算法辨识后件参数. 仿真结果表明,相比现有的自组织算法,本文提出的T-S模糊模型自组织算法在规则数较少的情况下,仍然具有较高的辨识精度和较好的泛化能力. 最后,利用提出的T-S模糊模型自组织算法较好地建立了直拉硅单晶炉加热器和空气预热器的温度模型.
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出版历程
  • 收稿日期:  2013-03-21
  • 修回日期:  2013-09-22
  • 刊出日期:  2013-12-20

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