K-Means clustering based Extreme Learning ANFIS with improved interpretability for regression problems
作者：
Highlights：
• K-Means++ clustering algorithm based input space partitioning of neuro-fuzzy network to reduce network complexity.
• Incorporated distinguishability constraints based on similarity indices between adjacent membership functions to improve interpretability.
• Faster training and less complex structure for the proposed neuro-fuzzy network.
• Insignificant reduction in accuracy of regression problems with improvement in interpretability.
摘要
•K-Means++ clustering algorithm based input space partitioning of neuro-fuzzy network to reduce network complexity.•Incorporated distinguishability constraints based on similarity indices between adjacent membership functions to improve interpretability.•Faster training and less complex structure for the proposed neuro-fuzzy network.•Insignificant reduction in accuracy of regression problems with improvement in interpretability.
论文关键词：Interpretability,Regression,K-means clustering
论文评审过程：Received 10 July 2020, Revised 10 November 2020, Accepted 2 January 2021, Available online 11 January 2021, Version of Record 18 January 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.106750
