ID 原文 译文
5784 最后在DMPC的基础上,采用随机决策序列下的贪婪迭代算法进行问题求解。 Finally, on the basis of DMPC, using random decision under the sequence of greed iterative algorithm for problem solving.
5785 并对所提方法的稳定性和收敛性进行分析。 And the stability and convergence of the proposed method is analyzed.
5786 同时通过设计仿真实验,验证了该方法的可行性和优越性。 At the same time through the design of simulation experiment, proves the feasibility and superiority of this method.
5787 针对响应共变特性的稳健参数设计问题,在多任务高斯过程(multi-task Gaussian processes,MTGP)建模框架下,结合质量损失函数和考虑响应不确定性的优化函数构建了一个考虑输出响应不确定性的MTGP(uncertainty of MTGP,UNMTGP)优化模型。 Robust response covariant properties parameters design problem, the multitasking Gaussian process (multi - task Gaussian the processes, MTGP) modeling framework, based on quality loss function and consider response of uncertainty optimization function built a MTGP considering the response output uncertainty (uncertainty of MTGP, UNMTGP) optimization model.
5788 首先,利用MTGP模型拟合实验数据,构建考虑响应间共变特性对优化结果影响的多元高斯模型。 First of all, using MTGP model fitted the experimental data, build considering the response characteristics of covariant on optimization results between multivariate gaussian model.
5789 其次,提出考虑输出响应不确定性的优化目标函数,构建多响应稳健优化模型。 Secondly, the paper puts forward the optimization of objective function considering the response output uncertainty, build response more robust optimization model.
5790 最后,结合全局优化方法,获得最优参数设计。 Finally, using the method of global optimization, to obtain the optimal parameter design.
5791 此外,结合真实案例,利用质量损失函数的相关评价指标,论证所提方法的有效性。 In addition, combined with real cases, the use of the related evaluation index of quality loss function, demonstrate the effectiveness of the proposed method.
5792 结果表明,所提方法考虑了响应共变特性和输出响应不确定性对优化结果的影响,有效改善了模型的预测质量,提升了输出响应的稳健性。 The results show that the proposed method considers the response covariant characteristics and output response, the influence of uncertainty on the optimized result can improve the quality of model predictions and enhance the robustness of the output response.
5793 针对现有的分层卷积特征跟踪算法在遭遇多种复杂环境时会发生跟踪失败的问题,提出一种空间注意机制下的自适应目标跟踪算法。 Tracking algorithm based on the existing hierarchical convolution tracking failure happens in a variety of complex environment problem, put forward a kind of spatial attention under the mechanism of adaptive target tracking algorithm.