Quantum Computing Based Machine Learning Method and Its Application in Radar Emitter Signal Recognition

  • Gexiang Zhang
  • Laizhao Hu
  • Weidong Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3131)


Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (QGA) and a good evaluation criterion is proposed to select the optimal feature subset from a large number of features extracted from radar emitter signals (RESs). The criterion function is given firstly. Then, detailed algorithm of QGA is described and its performances are analyzed. Finally, the best feature subset is selected from the original feature set (OFS) composed of 16 features of RESs. Experimental results show that the proposed approach reduces greatly the dimensions of OFS and heightens accurate recognition rate of RESs, which indicates that NFSA is feasible and effective.


Feature Selection Feature Subset Probability Amplitude Quantum Gate Feature Selection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gexiang Zhang
    • 1
    • 2
  • Laizhao Hu
    • 1
  • Weidong Jin
    • 2
  1. 1.National EW LaboratoryChengduChina
  2. 2.School of Electrical EngineeringSouthwest Jiaotong UniversityChengduChina

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