A Hybrid Neural Network Method for UAV Attack Route Integrated Planning

  • Authors:

  • Nan Wang;Xueqiang Gu;Jing Chen;Lincheng Shen;Min Ren

  • Affiliations:

  • Mechatronics and Automation School of National University of Defense Technology, Changsha, China 410073;Mechatronics and Automation School of National University of Defense Technology, Changsha, China 410073;Mechatronics and Automation School of National University of Defense Technology, Changsha, China 410073;Mechatronics and Automation School of National University of Defense Technology, Changsha, China 410073;Mechatronics and Automation School of National University of Defense Technology, Changsha, China 410073

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
  • Year:
  • 2009

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Abstract

This paper proposes a hybrid neural network method to solve the UAV attack route planning problem considering multiple factors. In this method, the planning procedure is decomposed by two planners: penetration planner and attack planner. The attack planner determines a candidate solution set, which adopts Guassian Radial Basis Function Neural Networks (RBFNN) to give a quick performance evaluation to find the optimal candidate solutions. The penetration planner adopts an alterative Hopfield Neural Network (NN) to refine the candidates in a fast speed. The combined effort of the two neural networks efficiently relaxes the coupling in the planning procedure and is able to generate a near-optimal solution within low computation time. The algorithms are simple and can easily be accelerated by parallelization techniques. Detailed experiments and results are reported and analyzed.