吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (04): 1064-1069.doi: 10.7964/jdxbgxb201304035

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Target threat assessment using glowworm swarm optimization and BP neural network

WANG Gai-ge1,2, GUO Li-hong1, DUAN Hong3, LIU Luo1,2, WANG He-qi1   

  1. 1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100039, China;
    3. School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
  • Received:2012-06-07 Online:2013-07-01 Published:2013-07-01

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Abstract:

Based on the introduction of Glowworm Swarm Optimization (GSO) and Back-Propagation (BP) neural network, a target threat assessment model is proposed and its algorithm is developed. This model is based on GSO optimized BP network (GSOBP). In GSOBP, GSO is employed to simultaneously optimize the initial weights and thresholds of the BP neural network. Target threat database is adopted to test the performance of GSOBP in target threat prediction. The performance of GSOBP is compared with that of normal BP neural network and Particle Swarm Optimization and Support Vector Machines (PSO_SVM). Experiment results show that target threat prediction accuracy by GSOBP is higher than that by normal BP or by PSO_SVM.

Key words: computer application, back propagation neural network, target threat assessment, glowworm swarm optimization

CLC Number: 

  • TP391.9

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