Fuzzy modeling based on feature selection and collaborative fuzzy clustering

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (19): 46-49.

• 理论研究 • Previous Articles Next Articles

Fuzzy modeling based on feature selection and collaborative fuzzy clustering

QI Hong-yu¹,WU Xiao-jun¹,WANG Shi-tong¹,YANG Jing-yu²

1.School of Information Engineering，Jiangnan University，Wuxi，Jiangsu 214122，China
2.School of Computer Science and Technology，Nanjing University of Science & Technology，Nanjing 210094，China

Received:2007-09-25 Revised:2007-12-11 Online:2008-07-01 Published:2008-07-01
Contact: QI Hong-yu

基于特征选择和协同模糊聚类的模糊建模研究

祁宏宇¹,吴小俊¹,王士同¹,杨静宇²

1.江南大学信息工程学院，江苏无锡 214122
2.南京理工大学计算机科学与技术学院，南京 210094

通讯作者: 祁宏宇

Abstract

Abstract: In order to improve the efficiency of fuzzy identification，a new approach to build fuzzy model is proposed.The approach is composed of two phases.The first one is to remove redundant information by feature selection approach using feature similarity.The second one is to identify the initial fuzzy system using the collaborative fuzzy clustering algorithm.The antecedent and consequent parameters of fuzzy model can be optimized.The collaborative fuzzy clustering is applied to extracted features to improve the parameters and efficiency of the fuzzy model.The results of experiments show the effectiveness of the proposed method for fuzzy modeling.

Key words: Takagi-Sugeno fuzzy model, collaborative fuzzy clustering, feature selection

摘要： 为了提高模糊模型辨识效率，提出了一种新的模糊模型建摸方法，该方法由两步组成：（1）采用基于特征相似性的特征选择方法，去除原始数据的冗余；（2）利用协同模糊聚类与G-K相结合的算法初始化模糊模型，使其前件和后件参数得到优化。采用该算法对有效的特征进行协同模糊聚类，模型参数得到改善，提高了模糊模型辨识的效率。模糊建模的实验结果表明了该方法的有效性。

关键词: T-S模糊模型, 协同模糊聚类算法, 特征选择

QI Hong-yu¹,WU Xiao-jun¹,WANG Shi-tong¹,YANG Jing-yu². Fuzzy modeling based on feature selection and collaborative fuzzy clustering[J]. Computer Engineering and Applications, 2008, 44(19): 46-49.

祁宏宇¹,吴小俊¹,王士同¹,杨静宇². 基于特征选择和协同模糊聚类的模糊建模研究[J]. 计算机工程与应用, 2008, 44(19): 46-49.

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Fuzzy modeling based on feature selection and collaborative fuzzy clustering

基于特征选择和协同模糊聚类的模糊建模研究

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