To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target...To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.展开更多
为解决不同人员相同操作的个体差异以及同一人员不同时间相同操作差异的问题,提出一种基于混合专家系统(mixture of experts,MoE)和长短期记忆神经网络(long short-term memory,LSTM)的倒闸操作识别方法MoE-LSTM。基于MoE对LSTM进行集成...为解决不同人员相同操作的个体差异以及同一人员不同时间相同操作差异的问题,提出一种基于混合专家系统(mixture of experts,MoE)和长短期记忆神经网络(long short-term memory,LSTM)的倒闸操作识别方法MoE-LSTM。基于MoE对LSTM进行集成,学习不同来源数据的特征分布。采集加速度动作数据构建倒闸操作数据集,基于滑动窗口对动作序列进行切分;将动作序列输入到MoE-LSTM中,由不同LSTM独立学习不同动作的时序依赖;通过门控网络选择对当前输入分类较好的LSTM的输出作为动作识别结果。仿真结果表明:不同LSTM对来自不同时空的动作数据都有擅长分类的特征空间。展开更多
基金Defense Industrial Technology Development Program (JCKY2020204B016)National Natural Science Foundation of China (92471206)。
文摘To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.
文摘为解决不同人员相同操作的个体差异以及同一人员不同时间相同操作差异的问题,提出一种基于混合专家系统(mixture of experts,MoE)和长短期记忆神经网络(long short-term memory,LSTM)的倒闸操作识别方法MoE-LSTM。基于MoE对LSTM进行集成,学习不同来源数据的特征分布。采集加速度动作数据构建倒闸操作数据集,基于滑动窗口对动作序列进行切分;将动作序列输入到MoE-LSTM中,由不同LSTM独立学习不同动作的时序依赖;通过门控网络选择对当前输入分类较好的LSTM的输出作为动作识别结果。仿真结果表明:不同LSTM对来自不同时空的动作数据都有擅长分类的特征空间。
