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中文摘要：
为了提高TSK（Takagi—Sugeno—Kang）模糊模型处理高维问题的推广能力，在结构风险最小化原则的基础上，提出了一种构造TSK模糊模型的新算法．该算法用GK（Gustafsonk—Kessel）算法确定模糊规则的前件隶属函数，然后用最小二乘支持向量回归机（LSSVR）确定模糊规则的后件参数．最小二乘支持向量回归机的核函数由模糊规则前件隶属函数生成，经证明它是Mercer核．实验结果表明，与现有算法相比，文中算法提高了TSK模糊模型处理高维问题的推广能力；与LSSVR相比，文中算法具有良好的鲁棒性．
英文摘要：
In order to improve the generalization capability of Takagi-Sugeno-Kang （TSK） fuzzy model in high-di- mension space, a novel algorithm of TSK model is proposed based on the structural risk minimization principle. In this algorithm, first, the antecedent membership functions of fuzzy rules are obtained by means of the Gustafson- Kessel （GK） algorithm. Next, the consequent parameters of fuzzy rules are determined by using the least-square support vector regression （LSSVR） machine. Then, the kernel function of LSSVR is deduced by the antecedent membership functions of fuzzy rules and is proved to be a Mercer kernel. Experimental results show that the pro- posed algorithm has better generalization capability than the conventional techniques of TSK model and is more ro- bust than LSSVR.
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