Micro-genetic algorithms for detecting and classifying electric power disturbances-Reference-Cited by-同舟云学术

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Micro-genetic algorithms for detecting and classifying electric power disturbances

Published:2016-05-26 Issue:S1 Volume:28 Page:379-392
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Jaen-Cuellar Arturo Yosimar,Morales-Velazquez Luis,Romero-Troncoso Rene de Jesus,Moriñigo-Sotelo Daniel,Osornio-Rios Roque Alfredo

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-016-2355-z/fulltext.html

Reference44 articles.

1. Ramesh M, Laxmi AJ (2012) Fault identification in HVDC using artificial intelligence—recent trends and perspective. In: International conference on power, signals, controls and computation (EPSCICON), pp 1–6. doi: 10.1109/EPSCICON.2012.6175256

2. Wulandhari LA, Wibowo A, Desa MI (2015) Condition diagnosis of multiple bearings using adaptive operator probabilities in genetic algorithms and back propagation neural networks. Neural Comput Appl 26:57–65. doi: 10.1007/s00521-014-1698-6

3. Raja MAZ (2014) Solution of the one-dimensional Bratu equation arising in the fuel ignition model using ANN optimised with PSO and SQP. Connect Sci 26(3):195–214. doi: 10.1080/09540091.2014.907555

4. Khan JA, Raja MAZ, Rashidi MM, Syam MI, Wazwaz AM (2015) Nature-inspired computing approach for solving non-linear singular Emden-Fowler problem arising in electromagnetic theory. Connect Sci 27(4):377–396. doi: 10.1080/09540091.2015.1092499

5. Raja MAZ (2014) Stochastic numerical techniques for solving Troesch’s problem. Inform Sci 279:860–873. doi: 10.1016/j.ins.2014.04.036

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