Evolving fuzzy predictor with multivariable Gaussian participatory learning and multi-innovations recursive weighted least squares: eFMI
Author:
Publisher
Springer Science and Business Media LLC
Subject
Control and Optimization,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12530-022-09421-9.pdf
Reference61 articles.
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2. Aliperti A, Bechini A, Marcelloni F, Renda A (2019) A fuzzy density-based clustering algorithm for streaming data. In: Proceedings of the IEEE international conference on fuzzy systems (FUZZ-IEEE). New Orleans, LA, USA. pp 1–6. https://doi.org/10.1109/FUZZ-IEEE.2019.8858909
3. Angelov P (2002) Evolving rule-based models: a tool for design of flexible adaptive systems. Springer, Berlin. https://doi.org/10.1007/978-3-7908-1794-2
4. Angelov P (2011) Fuzzily connected multimodel systems evolving autonomously from data streams. IEEE Trans Syst Man Cybern Part B (Cybernetics) 41(4):898–910. https://doi.org/10.1109/TSMCB.2010.2098866
5. Angelov P, Buswell R (2001) Evolving rule-based models: a tool for intelligent adaptation. In: Proceedings of the 9th Joint IFSA World Congress and 20th NAFIPS International Conference, vol 2. IEEE, pp 1062–1067. https://doi.org/10.1109/NAFIPS.2001.944752
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