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• W1972664823 abstract "Abstract This paper presents a newly developed neural network as applied to the problem of the estimation of the acting forces on radar targets such as reentry vehicles. The nonlinearities in the network are estimated, instead of being assumed as with conventional networks, using a novel approach based on optimal control and the method of invariant imbedding. No teaching signal is required since the network learns from the incoming radar observations and develops an estimate for the unknown forces. The neural network acts as an inverse dynamics model of the process that generated the radar observations. Thus, given noisy observation, the network successfully learns how to generate the proper forces that yielded these observations. A simple example, but yet realistic, of the target dynamics is given, and Monte Carlo simulation is performed. The estimates of the unknown forces are obtained by the neural network and it is shown that they are very close to the real forces." @default.
• W1972664823 created "2016-06-24" @default.
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• W1972664823 date "1991-01-01" @default.
• W1972664823 modified "2023-09-27" @default.
• W1972664823 title "A neural network for the estimation of forces acting on radar targets" @default.
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• W1972664823 doi "https://doi.org/10.1016/0893-6080(91)90020-6" @default.
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