基于核映射的高阶Takagi-Sugeno模糊模型
Higher-order Takagi-Sugeno fuzzy model based on kernel mapping
摘要点击 1516  全文点击 1261  投稿时间:2010-02-04  修订日期:2010-05-31
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DOI编号  
  2011,28(5):681-687
中文关键词  模糊系统  模糊聚类  支持向量机  核函数
英文关键词  fuzzy systems  fuzzy clustering  support-vector-machine  kernel function
基金项目  国家自然科学基金资助项目(60433020, 10471045, 61070033); 广东省自然科学基金资助项目(031360, 04020079); 广东省自然科学基金重点项目(9251009001000005).
作者单位E-mail
蔡前凤 广东工业大学 应用数学学院 caiqianfeng@163.com 
郝志峰 广东工业大学 计算机学院  
杨晓伟 华南理工大学 理学院  
中文摘要
      本文研究规则后件为非线性函数的高阶Takagi-Sugeno(TS)模糊系统. 为求解规则后件的函数表达式, 首先通过一个核映射将原输入空间映射到高维特征空间, 使原空间的非线性子模型转化为高维特征空间的线性子模型, 获得了规则后件的非线性函数的计算公式. 然后, 给出了用核模糊聚类和最小二乘支持向量机设计模糊系统的一种新算法. 最后通过4个公开数据集上的仿真实验验证了所提算法的逼近能力、推广能力和鲁棒性能.
英文摘要
      This paper is concerned with higher-order Takagi-Sugeno(TS) fuzzy systems, where the consequent of a fuzzy rule is a nonlinear combination of input variables. To solve this problem, an implicit nonlinear kernel-mapping is introduced to map the original input space to some higher dimensional feature space, where locally nonlinear submodels of TS fuzzy systems are transformed into locally linear submodels; and then, the expressions of the consequent functions are presented. Furthermore, a novel algorithm of designing higher-order TS fuzzy systems is developed by combining the kernel-based fuzzy clustering with least squares support-vector-machines(LSSVM). Finally, the approximation accuracy, the generalization ability and robustness of the proposed algorithm have been demonstrated by simulation experiments on four well-known data sets.