A type-2 fuzzy c-regression clustering algorithm for Takagi-Sugeno system identification and its application in the steel industry
A type-2 fuzzy c-regression clustering algorithm for Takagi-Sugeno system identification and its application in the steel industry
复制标题
DOI：
10.1016/j.ins.2011.10.015
复制
发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
M. Zarandi;R. Gamasaee;I. Türksen
中科院分区：
文献类型：
--
作者：
M. Zarandi;R. Gamasaee;I. Türksen
针对T-S系统的结构识别阶段，提出了一种新的2型模糊c-回归聚类算法。我们用模糊参数“m”来表示不确定性。为了识别区间2型模糊集的参数，使用了两个模糊控制器m1和m2。然后，在模糊c回归聚类算法中利用这两个模糊指标，生成区间2型模糊隶属函数。本文提出的模型是将1型FCRM算法[25]扩展为区间2型模糊模型。采用高斯混合模型建立模糊c回归聚类算法的划分矩阵。最后，为了验证所提出的模型，给出了几个数值算例。该模型在加拿大一家钢铁公司的实际数据集上进行了测试。计算结果表明，与1型NFCRM和多元回归相比，该模型具有更强的鲁棒性和减小误差的能力。
This paper proposes a new type-2 fuzzy c-regression clustering algorithm for the structure identification phase of Takagi–Sugeno (T–S) systems. We present uncertainties with fuzzifier parameter “m”. In order to identify the parameters of interval type-2 fuzzy sets, two fuzzifiers “m1” and “m2” are used. Then, by utilizing these two fuzzifiers in a fuzzy c-regression clustering algorithm, the interval type-2 fuzzy membership functions are generated. The proposed model in this paper is an extended version of a type-1 FCRM algorithm [25], which is extended to an interval type-2 fuzzy model. The Gaussian Mixture model is used to create the partition matrix of the fuzzy c-regression clustering algorithm. Finally, in order to validate the proposed model, several numerical examples are presented. The model is tested on a real data set from a steel company in Canada. Our computational results show that our model is more effective for robustness and error reduction than type-1 NFCRM and the multiple-regression.