摘要
针对复杂空间信息网络系统仿真建模研究的问题,提出了一种基于仿真数据驱动的空间信息网络建模方法,设计了空间信息网络性能指标的深度自编码网络和随机森林回归的混合预测分析模型。针对空间信息网络性能指标维度高,样本分布宽的特点,利用深度学习自编码网络进行空间信息网络性能指标编码解码网络的构建,并结合随机森林回归建立空间信息网网络设计参数到性能指标的关系模型。最后通过案例分析,表明该混合模型既能直接预测空间信息网络性能指标,又可以对网络设计参数进行灵敏度分析。
Aiming at the problem for the research on the simulation and modeling of the space information network （SIN） with high complexity, a SIN modeling approach based on simulation data is proposed. On this basis, a deep predictive auto-encoder network for SIN performance indicators and a hybrid forecasting analysis model with random forest regression are designed. Due to the characteristics of high dimensionality and wide distribution of SIN performance indicators, with using the deep learning auto-encoder network, the construction of SIN performance index is realized. Combined with the random forest regression model, the relationship model between the network design parameters of SIN and the performance indicators is built. The case study shows that the hybrid model can not only directly predict the SIN performance indicators, but also can analyze the sensitivity of the network design parameters.
作者
杨兴
吴静
周建国
江昊
朱劼
Yang Xing;Wu Jing;Zhou Jianguo;Jiang Hao;Zhu Jie(School of Electronics and Information,Wuhan University,Wuhan 430072,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第11期4323-4333,4339,共12页
Journal of System Simulation
基金
国家重点研发计划课题(2017YFB0504103)
关键词
仿真数据驱动
空间信息网建模
深度自编码网络
随机森林回归
simulation data driven
space information network modeling
deep auto-encoder network
random forest regression
