DocumentCode
2327470
Title
Detecting failure of antenna array elements using machine learning optimization
Author
Xu, Nan ; Christodoulou, C.G. ; Barbin, S.E. ; Martinez-Ramón, M.
Author_Institution
Univ. of New Mexico, Albuquerque
fYear
2007
fDate
9-15 June 2007
Firstpage
5753
Lastpage
5756
Abstract
A Multi-class support vector classifier (SVC) is proposed for planar array failure diagnosis. Extracted feature information from the far field intensity of the array is used to train and test the multi-class SVC, so one can detect the location of failed elements in an array and also the level of failure.
Keywords
antenna arrays; electronic engineering computing; learning (artificial intelligence); support vector machines; antenna array elements; detecting failure; machine learning optimization; multi class; planar array failure diagnosis; support vector classifier; Antenna arrays; Antenna measurements; Data mining; Feature extraction; Lagrangian functions; Machine learning; Static VAr compensators; Support vector machine classification; Support vector machines; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Antennas and Propagation Society International Symposium, 2007 IEEE
Conference_Location
Honolulu, HI
Print_ISBN
978-1-4244-0877-1
Electronic_ISBN
978-1-4244-0878-8
Type
conf
DOI
10.1109/APS.2007.4396858
Filename
4396858
Link To Document