摘要
从实际检测数据中提取模糊规则进而建立有效的模糊模型对实现复杂系统的智能建模与控制具有重要意义.在一些文献中对该问题进行了较深入的研究,并提出了有效的从数值数据中提取模糊规则的算法(简称为WM和iWM算法).对WM和iWM算法的进一步分析研究表明,该算法在完备性和鲁棒性方面还有进一步改进的可能.本文采用数据挖掘技术提出一个改进的提取模糊规则的算法(简称DM算法)并在完备性和鲁棒性方面与WM和iWM算法进行了比较研究.模糊建模实例表明,本文提出的DM算法具有更好的逼近能力和对不确定数据干扰的鲁棒性.
Extraction of fuzzy rules from numerical data for fuzzy modeling and control is significant.Such a problem has received considerable attention in the past and some algorithms,termed as the WM algorithm and the iWM algorithm,have been proposed in the literature.Research on the WM algorithm and the iWM algorithm showed that some improvements on robustness and completeness of these algorithms could be done.This paper aims to develop an improved fuzzy rule extraction algorithm（termed as the DM algorithm） using data mining techniques,and the completeness and the robustness of rule-base for fuzzy modeling with noisy data are addressed.Some illustrative examples are given.Results demonstrate that our proposed rule extraction algorithm outperforms the WM algorithm and iWM algorithm in terms of both modeling accuracy and robustness with respect to noisy data.
出处
《自动化学报》
EI
CSCD
北大核心
2010年第9期1337-1342,共6页
Acta Automatica Sinica
基金
国家自然科学基金(50875042
60821063
50875039)
高档数控机床与基础制造装备(科技重大专项课题)(2010ZX04014-014)资助~~
