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      A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine

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          Abstract

          Radar target classification is very important in military and civilian fields. Extreme Learning Machines (ELMs) are widely used in classification because of their fast learning speed and good generalization performance. However, because of their shallow architecture, ELMs may not effectively capture the data high level abstractions. Although many researchers have proposed the Deep Extreme Learning Machine (DELM), which can be used to automatically learn high level feature representations, the model easily falls into overfitting when the training sample is limited. To address this issue, Dropout Constrained Deep Extreme Learning Machine (DCDELM) is proposed in this paper. The experimental results on the measured radar data show that the accuracy of the proposed algorithm can reach 93.37%, which is 5.25% higher than that of the stacked autoencoder algorithm, and 8.16% higher than that of the traditional DELM algorithm.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 October 2018
          : 7
          : 5
          : 613-621
          Affiliations
          [1 ] (College of Electronic Science, National University of Defense Technology, Changsha 410073, China)
          Article
          7bbfa29a099c427cb2e87e99c03cbabb
          10.12000/JR18048

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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