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
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;
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
DOI :
10.1109/APS.2007.4396858
