A Study on the Hopfield Network for automatic weapon assignment

자동무장할당을 위한 홉필드망 설계연구

  • 이양원 (호남대학교 정보통신공학과) ;
  • 강민구 (호남대학교 정보통신공학과) ;
  • 이봉기 (국방과학연구소)
  • Published : 1997.12.01

Abstract

A neural network-based algorithm for the static weapon-target assignment (WTA) problem is Presented in this paper. An optimal WTA is one which allocates targets to weapon systems such that the total expected leakage value of targets surviving the defense is minimized. The proposed algorithm is based on a Hopfield and Tank's neural network model, and uses K x M processing elements called binary neuron, where M is the number of weapon platforms and K is the number of targets. From the software simulation results of example battle scenarios, it is shown that the proposed method has better performance in convergence speed than other method when the optimal initial values are used.

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