Robust data expansion for optimised modelling using adaptive neuro-fuzzy inference systems
作者：
Highlights：
• ANFIS networks capture complexities not easily found using traditional ANNs.
• Model complexity versus data sample size is a significant practical problem.
• Data augmentation methods can be constructed using robust continuous models.
• Multiquadric RBFs can be easily tuned to provide robust, accurate data expansion.
• Complex models can be built with confidence using reliable data expansion.
摘要
•ANFIS networks capture complexities not easily found using traditional ANNs.•Model complexity versus data sample size is a significant practical problem.•Data augmentation methods can be constructed using robust continuous models.•Multiquadric RBFs can be easily tuned to provide robust, accurate data expansion.•Complex models can be built with confidence using reliable data expansion.
论文关键词：Robust data expansion,Network complexity,Data augmentation,RBF interpolation,ANFIS modelling,Improved generalisation
论文评审过程：Received 6 April 2020, Revised 13 January 2021, Accepted 20 October 2021, Available online 30 October 2021, Version of Record 8 November 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116138
