LECTURE NOTES IN COMPUTER SCIENCE

Yumuşak, Nejat; Temurtaş, Feyzullah

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dc.contributor.authors	Temurtas, F; Gunturkun, R; Yumusak, N; Temurtas, H; Unsal, A;
dc.date.accessioned	2020-01-13T07:57:00Z
dc.date.available	2020-01-13T07:57:00Z
dc.date.issued	2004
dc.identifier.citation	Temurtas, F; Gunturkun, R; Yumusak, N; Temurtas, H; Unsal, A; (2004). LECTURE NOTES IN COMPUTER SCIENCE. INNOVATIONS IN APPLIED ARTIFICIAL INTELLIGENCE, 3029, 1052-1043
dc.identifier.isbn	3-540-22007-0
dc.identifier.issn	0302-9743
dc.identifier.uri	https://hdl.handle.net/20.500.12619/2467
dc.description.abstract	In this study, the method to apply the Elman's recurrent neural networks for harmonic detection process in active filter is proposed. The feed forward neural networks were also used for comparison. We simulated the distorted wave including 5(th), 7(th), 11(th), 13(th) harmonics and used them for training of the neural networks. The distorted wave including up to 25(th) harmonics were prepared for testing of the neural networks. Elman's recurrent and feed forward neural networks were used to recognize each harmonic. The results show that these neural networks are applicable to detect each harmonic effectively.
dc.language	English
dc.publisher	SPRINGER-VERLAG BERLIN
dc.subject	Robotics
dc.title	LECTURE NOTES IN COMPUTER SCIENCE
dc.type	Proceedings Paper
dc.identifier.volume	3029
dc.identifier.startpage	1043
dc.identifier.endpage	1052
dc.contributor.department	Sakarya Üniversitesi/Bilgisayar Ve Bilişim Bilimleri Fakültesi/Bilgisayar Mühendisliği Bölümü
dc.contributor.saüauthor	Yumuşak, Nejat
dc.contributor.saüauthor	Temurtaş, Feyzullah
dc.relation.journal	INNOVATIONS IN APPLIED ARTIFICIAL INTELLIGENCE
dc.identifier.wos	WOS:000221714200107
dc.contributor.author	Yumuşak, Nejat
dc.contributor.author	Temurtaş, Feyzullah