A novel and fast MIMO fuzzy inference system based on a class of fuzzy clustering algorithms with interpretability and complexity analysis
作者：
Highlights：
• CFIS is more accurate and faster than other FISs.
• CFIS is more interpretable and less complex than other FISs.
• Fuzzy clusters on dense regions of data in input space serve as CFIS rules.
• THEN part of CFIS accommodates any function of input variables.
• IF part of CFIS needs no parameter identification.
摘要
•CFIS is more accurate and faster than other FISs.•CFIS is more interpretable and less complex than other FISs.•Fuzzy clusters on dense regions of data in input space serve as CFIS rules.•THEN part of CFIS accommodates any function of input variables.•IF part of CFIS needs no parameter identification.
论文关键词：Fuzzy systems,Fuzzy clustering,TS fuzzy system,Classification,Regression,Interpretability,Complexity
论文评审过程：Received 5 February 2017, Revised 3 April 2017, Accepted 22 April 2017, Available online 24 April 2017, Version of Record 23 May 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.04.045
