ID 原文 译文
55697 算法以root-RARE与多重信号分类算法(multiple signal classification,MUSIC)联合解模糊,以root-RARE得到的粗估计为参考,解整个非同构分布式阵列MUSIC谱估计的模糊,从而得到高精度无模糊的估计。 Algorithm as root - RARE and multiple signal classification algorithm (multiple signal classification, MUSIC) combined fuzzy solution, as root coarse estimation of the RARE available for reference, the solution of the non homogeneous distributed array MUSIC spectrum estimation of fuzzy, and high precision without fuzzy estimate is obtained.
55698 推导非同构分布式阵列方向估计的克拉美罗界,分析算法的波达方向估计性能,同时分析分布式阵列方向估计时的基线模糊门限与信噪比门限之间的关系。 Non homogeneous distributed array direction estimation is derived, Latin American community, analysis algorithm of doa estimation performance, at the same time analysis the distributed array direction estimation of baseline fuzzy threshold and the relationship between the SNR threshold.
55699 仿真结果验证所提算法方向估计的正确性及有效性。 The simulation results verify the correctness and effectiveness of the direction of the proposed algorithm estimates.
55700 区别模型更新(discriminative model update,DMU)是一种常用的求解交互式动态影响图(interactive dynamic influence diagrams,I-DIDs)问题的算法。 Difference model updates (discriminative model update, DMU) is a kind of commonly used to solve the interactive dynamic influence diagram (interactive dynamic influence diagrams, I - DIDs) algorithm of the problem.
55701 结合lookahead思想提出了一种判断模型近似行为等价的改进DMU方法。 A judgment model was put forward with the combination of lookahead thought approximate behavior equivalent to improve the method of DMU.
55702 所提方法首先将满足近似行为等价的模型聚类形成代表模型集合,然后自上而下对代表模型进行更新,在模型更新过程中,只更新那些与其他模型预测行为不同的模型。 The proposed method first will meet the approximate behavior equivalence model clustering form on behalf of the collection, and then from top to bottom to represent model update, in the process of model updates, update only those different model and other models to predict behavior.
55703 结合lookahead思想提出了一种判断模型近似行为等价的方法。 A judgment model was put forward with the combination of lookahead thought approximate behavior equivalence method.
55704 与DMU算法相比,该算法能迅速有效地减少模型的数量,从而减少了计算机的存储空间和运行时间,提高了算法的效率。 Compared with the DMU algorithm, this algorithm can effectively reduce the number of model rapidly, thus reducing the computer storage space and running time, improve the efficiency of the algorithm.
55705 最后通过对多Agent老虎问题及机器维修问题实验来验证所提方法的有效性。 Finally through the Agent tiger and machine maintenance problems more experiments to verify the effectiveness of the method.
55706 针对系统模型同时具有未知输入扰动和模型不确定性,提出了一种鲁棒故障检测观测器的设计方法。 Model of the system with unknown input disturbance and model uncertainty at the same time, this paper proposes a design method of robust fault detection observer.