INFGMN – Incremental Neuro-Fuzzy Gaussian mixture network

作者:

Highlights:

• A NFS that learns incrementally using a single scan over the training data.

• The learning process can proceed in perpetuity as new training data become available.

• A Mamdani–Larsen fuzzy rule base is defined automatically and incrementally.

• Attempts to provide the best trade-off between accuracy and interpretability.

• Is unaffected by catastrophic interference (Stability-Plasticity dilemma).

摘要

•A NFS that learns incrementally using a single scan over the training data.•The learning process can proceed in perpetuity as new training data become available.•A Mamdani–Larsen fuzzy rule base is defined automatically and incrementally.•Attempts to provide the best trade-off between accuracy and interpretability.•Is unaffected by catastrophic interference (Stability-Plasticity dilemma).

论文关键词:Incremental learning,Mamdani–Larsen-type fuzzy,Stability-Plasticity dilemma,Accuracy-Interpretability dilemma,Neuro-Fuzzy system

论文评审过程:Received 10 May 2017, Revised 20 July 2017, Accepted 21 July 2017, Available online 22 July 2017, Version of Record 28 July 2017.

论文官网地址:https://doi.org/10.1016/j.eswa.2017.07.032