Neural networks for online learning of non-stationary data streams: a review and application for smart grids flexibility improvement-Reference-Cited by-同舟云学术

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Neural networks for online learning of non-stationary data streams: a review and application for smart grids flexibility improvement

Published:2020-05-17 Issue:8 Volume:53 Page:6111-6154
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Hammami Zeineb,Sayed-Mouchaweh Moamar,Mouelhi Wiem,Ben Said Lamjed

Funder

Smart Lab

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-020-09844-3.pdf

Reference82 articles.

1. Alippi C, Roveri M (2008) Just-in-time adaptive classifiers: part I: detecting nonstationary changes. IEEE Trans Neural Netw 19(7):1145–1153

2. Aljundi R, Lin M, Goujaud B, Bengio Y (2019) Online continual learning with no task boundaries. CoRR arXiv:1903.08671

3. Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, pp 1–16

4. Bakakeu J, Tolksdorf S, Bauer J, Klos HH, Peschke J, Fehrle A, Eberlein W, Bürner J, Brossog M, Jahn L, Franke J (2018) An artificial intelligence approach for online optimization of flexible manufacturing systems. In: Energy efficiency in strategy of sustainable production IV. Applied Mechanics and Materials, vol 882, pp 96–108

5. Bessa RJ, da Costa Miranda V, da Gama JMP (2009) Entropy and correntropy against minimum square error in offline and online three-day ahead wind power forecasting. IEEE Trans Power Syst 24:1657–1666

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