A novel approach for neuro-fuzzy system-based multi-objective optimization to capture inherent fuzziness in engineering processes

作者：

Highlights：

• A novel approach for capturing fuzziness of the data related to manufacturing process.

• Fuzziness of the experimental data has been modeled using neuro-fuzzy system (NFS).

• Input–output relationships are determined through NFS-based multi-objective optimization.

• Performance of the developed approach has been tested on two problems.

摘要

•A novel approach for capturing fuzziness of the data related to manufacturing process.•Fuzziness of the experimental data has been modeled using neuro-fuzzy system (NFS).•Input–output relationships are determined through NFS-based multi-objective optimization.•Performance of the developed approach has been tested on two problems.

论文关键词：Multi-objective optimization,Input–output relationships,Neuro-fuzzy system,Clustering,Regression analysis

论文评审过程：Received 18 August 2018, Revised 12 March 2019, Accepted 15 March 2019, Available online 20 March 2019, Version of Record 26 April 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.03.017