Air defense missile-target allocation models for a naval task group
Computers and Operations Research
Adaptive dynamic programming: an introduction
IEEE Computational Intelligence Magazine
CORALS: A real-time planner for anti-air defense operations
ACM Transactions on Intelligent Systems and Technology (TIST)
Optimal control for boiler combustion system based on iterative heuristic dynamic programming
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
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This paper proposes a solution methodology for a missile defense problem involving the sequential allocation of defensive resources over a series of engagements. The problem is cast as a dynamic programming/Markovian decision problem, which is computationally intractable by exact methods because of its large number of states and its complex modeling issues. We employed a neuro-dynamic programming framework, whereby the cost-to-go function is approximated using neural network architectures that are trained on simulated data. We report on the performance obtained using several different training methods, and we compare this performance with the optimal approach