Fast detection and location of failed array elements using the fast SVM algorithm
- 1 July 2010
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2010 14th International Symposium on Antenna Technology and Applied Electromagnetics & the American Electromagnetics Conference
Abstract
With the increased proliferation of array antennas, for a myriad of wireless applications, the monitoring and correction of array element failure is becoming more critical. In this paper, a fast Support Vector Machine (SVM) classifier, based on low-degree polynomial kernel is developed, to expeditiously detect and locate the failed elements of an arbitrary analog array, while categorizing the level of failure, at the same time. This method will work on any analog array configuration, and the results for a linear array sample are illustrated to demonstrate its effectiveness.Keywords
This publication has 13 references indexed in Scilit:
- Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairsBioinformatics, 2007
- Detecting failure of antenna array elements using machine learning optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Finding failed element positions in linear antenna arrays using neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- On array failure mitigation using genetic algorithms and a priori joint optimizationIEEE Antennas and Propagation Magazine, 2005
- Array failure correction with orthogonal methodPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- A comparison of methods for multiclass support vector machinesIEEE Transactions on Neural Networks, 2002
- Support vector machines for histogram-based image classificationIEEE Transactions on Neural Networks, 1999
- Support-vector networksMachine Learning, 1995
- Source synthesis for training array neural networksElectronics Letters, 1995
- A training algorithm for optimal margin classifiersPublished by Association for Computing Machinery (ACM) ,1992