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针对如何从基于分布式并行计算获得的数千个飞船返回过程预报落点中优选出“最可能”落点这一问题,在充分考虑以往返回落点计算的先验知识和最新落点信息的基础上,设计了飞船返回的自适应聚类模糊系统,采用含有时间因子的迭代自组织数据分析算法研究了返回舱落点优选方法。最后,利用“神舟八号”返回落点实测数据进行模型检验。计算结果表明,该方法的落点预报精度较传统选优算法的预报精度高50%以上,同时具有较好的稳定性,可为飞船返回搜救提供技术支撑。
Aiming at the problem of selecting the most likely place in the forecasting process of the thousands of spacecraft return processes based on distributed parallel computing, taking into account the priori knowledge and the latest placement of the past and return points Based on the information, an adaptive clustering fuzzy system for spacecraft return is designed. The iterative self-organizing data analysis algorithm with time factor is applied to study the optimal method of returning capsule. Finally, using “Shenzhou VIII” to return the measured data to verify the model. The calculation results show that the accuracy of the method is better than that of the traditional optimization algorithm by 50%, and it also has better stability. It can provide technical support for the search and rescue of the spacecraft.