Identification of switched reluctance machine using fuzzy model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-022-01749-4.pdf
Reference67 articles.
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2. Abonyi J, Babuška R, Verbruggen HB, Szeifert F (2000b) Incorporating prior knowledge in fuzzy model identification. Int J Syst Sci 31(5):657–667. https://doi.org/10.1080/002077200290966
3. Aguado-Rojas M, Maya-Ortiz P, Espinosa-Pérez G (2018) On-line estimation of switched reluctance motor parameters. Int J Adapt Control Signal Process 32(6):950–966. https://doi.org/10.1002/acs.2877
4. Alci M, Asyali MH (2009) Nonlinear system identification via laguerre network based fuzzy systems. Fuzzy Sets Syst 160(24):3518–3529. https://doi.org/10.1016/j.fss.2009.09.016
5. Amrita, Gupta P, Singh P (2021) Priority-wise test case allocation using fuzzy logic. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-021-01247-z
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