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。
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
出处
《计算机工程与应用》
CSCD
北大核心
2008年第19期46-49,共4页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60472060,No.60572034)
江苏省自然科学基金(the Natural Science Foundation of Jiangsu Province of China under Grant No.BK2006081)
2006年教育部新世纪优秀人才计划项目(Program for New Century Excellent Talents in University of China)
关键词
T—S模糊模型
协同模糊聚类算法
特征选择
Takagi-Sugeno fuzzy model
collaborative fuzzy clustering
feature selection
