作　　者： ; ; ;

机构地区： 华南理工大学计算机科学与工程学院

出　　处： 《模式识别与人工智能》 2009年第3期411-416,共6页

摘　　要： 为了提高模糊系统处理高维问题的推广能力与鲁棒性能,提出将模糊聚类和支持向量机算法结合起来构造TSK模糊系统的算法.首先运用模糊聚类算法对输入空间进行划分,确定模糊规则前件的隶属函数.然后用支持向量机算法确定模糊规则的后件参数.该支持向量机的核函数是由模糊规则前件的隶属函数构造的,并且是Mer-cer核.在3个数据集的实验结果表明,与TSK模糊系统的传统算法和支持向量机相比较,本文算法具有更好的推广能力和鲁棒性. An algorithm is presented to design a Takagi-Sugeno-Kang （TSK） fuzzy system with good generalization ability and robustness in high dimensional feature space by fuzzy clustering algorithms and support vector machines （SVM）. Firstly, the antecedent membership functions are obtained by fuzzy clustering algorithms in the product space of the input variables. Then, the corresponding consequent parameters of the TSK model can be estimated from data using SVM. The kernel function of the proposed algorithm can be generated by the antecedent membership functions and it is proved to be a Mercer kernel. Finally, experimental results of three well-known datasets show that the proposed method has better generalization ability and robustness than the traditional techniques of TSK model and SVM.

关 键 词： 模糊系统 模糊规则 支持向量 结构风险 损失函数

领　　域： [自动化与计算机技术] [自动化与计算机技术]