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首页> 外文会议>Symposium of the International Academy of Astronautics, Oct 2-6, 2000, Rio de Janeiro, Brazil >ARTIFICIAL NEURAL NETWORK ANALYSIS OF SPACE DEBRIS
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ARTIFICIAL NEURAL NETWORK ANALYSIS OF SPACE DEBRIS

机译：空间碎片的人工神经网络分析

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Models of the current and future space debris environment typically rely on simple isotropic break-up models for computational efficiency. However, historical break-up events (explosions and hypervelocity collisions) often result in an asymmetric dispersion of fragments that cannot be described by these simple models. This paper presents the investigation of new strategies that use artificial neural networks for analysing asymmetric break-up events in three scenarios. The first scenario was 'explosion classification'. A neural network pattern recognition algorithm is developed to classify fragment orbital data into one of three explosion categories (isotropic, radially enhanced or tangentially enhanced). This had a correct classification performance of 97% using the mean and standard deviation of fragments' apogees, perigees, inclinations, eccentricities and periods as inputs. In the second and third scenarios, continuous neural network models of (a) the ejection vector in an anisotropic explosion and (b) the relative velocity vector of a projectile in a hypervelocity collision were developed. The components of the ejection vector (scenario 2) were predicted with an average root mean square error of 0.43 and the components of the projectile velocity vector (scenario 3) were predicted with an average root mean square error of 2.45 km/s. For all three scenarios, break-ups were simulated and fragment orbits evolved using the Space Debris Simulation (SDS) software.
机译：当前和未来的空间碎片环境模型通常依赖于简单的各向同性分解模型来提高计算效率。但是，历史性破裂事件（爆炸和超高速碰撞）通常会导致碎片的不对称分散，而这些简单模型无法描述这些碎片。本文介绍了在三种情况下使用人工神经网络分析不对称破裂事件的新策略的研究。第一种情况是“爆炸分类”。开发了神经网络模式识别算法，以将碎片轨道数据分类为三个爆炸类别（各向同性，径向增强或切向增强）之一。使用碎片的顶点，边缘，倾斜度，偏心率和周期的平均值和标准偏差作为输入，它的正确分类性能为97％。在第二和第三种情况下，开发了连续的神经网络模型，该模型是（a）各向异性爆炸中的弹射矢量和（b）超高速碰撞中的弹丸的相对速度矢量。弹射速度矢量的分量（场景2）的平均均方根误差为0.43，而弹丸速度矢量的分量（场景3）的平均均方根误差为2.45 km / s。对于这三种情况，都使用空间碎片模拟（SDS）软件模拟了破碎过程并演化了碎片轨道。

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