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The trajectory of an artificial rocket is a non-linear equation of motion which can be solved in various method for instance by the method of an applied newton's law used for establish the equation of motion or by the numerical method of Runge Kutta 4th order. All of the equations of motion are modified from the principle of Modified Point Mass Trajectory Model (MPMTM) which will have to compare its mathematic solution. These methods are complicated to calculate and can cause an error. Therefore, in this article will present the application of back-propagation neural networks to predict the trajectory of a rocket in various input according to the actual usage conditions, which 5 inputs. The performance index is used to determine the optimal neural network. The results, the most efficient system identification neural network that has a performance index of 0.04 and this neural network will represent the trajectory of an artificial rocket instead of complex equations which will simplify and takes less time to calculate.
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