信阳师范学院学报（自然科学版）Issue(1)：124-128,5.DOI:10.3969/j.issn.1003-0972.2016.01.030
基于正则化的模糊 C-均值聚类算法及其在T-S 模糊系统辨识问题中的应用
Fuzzy C-means Clustering Algorithm Based Regularization and Its Application in the Problem of T-S Fuzzy System Identification
摘要
Abstract
A new fuzzy C‐means clustering algorithm (RBFCM ,Regularization based Fuzzy C‐means) algo‐rithm was established by adding a regularization functional ,which was constructed by the errors of clustering centers ,in the objective function of fuzzy C‐means clustering algorithm .Algorithm RBFCM could not only a‐chieve high clustering accuracy ,but also stable the computed results .Furthermore ,the obtained RBFCM algo‐rithm was applied in T‐S fuzzy model based on system identification problem .Because of the optimized partition of the input space and the improved membership functions ,the accuracy of the solution and the convergence speed of the followed T‐S fuzzy system identification process were improved too .Finally ,the validity and ad‐vances of RBFCM algorithm were illustrated by the cluster examples of IRIS data set and the noised IRIS data set and the identification example of Box‐Jenkins gas furnace data set .关键词
模糊聚类/正则化/模糊模型/系统辨识Key words
fuzzy clustering/regularization/fuzzy modeling/system identification分类
信息技术与安全科学引用本文复制引用
王艳,徐再花,张大庆..基于正则化的模糊 C-均值聚类算法及其在T-S 模糊系统辨识问题中的应用[J].信阳师范学院学报（自然科学版）,2016,(1):124-128,5.基金项目
国家自然科学基金项目 （）
