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Numerical Optimization of Flight Trajectory for Rockets via Artificial Neural Networks

机译:人工神经网络的火箭飞行轨迹数值优化。

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This research arise to optimize the flight trajectory for rockets, for this were applied hybrid techniques, based on the Finite Difference Method (FDM) to obtain the solution of the non-linear differential equations provided by the analytic modeling. So aiming at the optimizations were applied the Artificial Neural Networks (ANN) into two curves of thrust rocket engines, in which was possible to adjust the temporal discretization. The results showed that using ANN, the accuracy increased 26 times relative to the non-optimized results, also to compare with commercial software the biggest error found was 10%. Therefore, it was proven that when applying the ANN that provided excellent results with lower computational cost.
机译:这项研究的出现是为了优化火箭的飞行轨迹,为此,在有限差分法(FDM)的基础上,应用混合技术获得了解析模型提供的非线性微分方程的解。因此针对优化,将人工神经网络(ANN)应用于推力火箭发动机的两条曲线,从而可以调整时间离散。结果表明,使用人工神经网络,其精度相对于非优化结果提高了26倍,与商用软件相比,发现的最大误差为10%。因此,已经证明,当应用人工神经网络时,可以以较低的计算成本提供出色的结果。

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