Neural Network Parallel Algorithm for Missile Interceptor Allocation Problem

Kanae Yamaoka, Keisuke Iwai, Hidema Tanaka, Takakazu Kurokawa


This paper proposes Hopfield type neural network architecture consisting of binary neurons for missile interceptor allocation problem, which is a kind of Weapon Target Assignment Problem, and is a significant problem in military operation field. Through a large number of simulation runs, the proposed neural network architecture could find efficient assignment schedules within several milliseconds. Compared with former works, the developed neural network simulator could find out allocation results with higher quality. Furthermore, its simulation results showed high searching abilities for optimum, or near optimum solutions.


neural network; parallel; binary; missile interceptor; allocation; weapon target

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