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2018影响因子

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## 留言板

 引用本文: 高迎彬, 徐中英. 基于加权矩阵的多维广义特征值并行分解算法. 自动化学报, 2020, 41(x): 1−7
Gao Ying-Bin, Xu Zhong-Ying. Multiple generalized eigenvalue decomposition algorithm in parallel based on weighted matrix. Acta Automatica Sinica, 2020, 41(x): 1−7 doi: 10.16383/j.aas.c200399
 Citation: Gao Ying-Bin, Xu Zhong-Ying. Multiple generalized eigenvalue decomposition algorithm in parallel based on weighted matrix. Acta Automatica Sinica, 2020, 41(x): 1−7

## Multiple Generalized Eigenvalue Decomposition Algorithm in Parallel Based on Weighted Matrix

Funds: Supported by National Natural Science Foundation of P. R. China (61673387, 61903375)
• 摘要: 针对串行广义特征值分解算法实时性差的缺点, 提出了基于加权矩阵的多维广义特征值分解算法. 与串行算法不同, 所提算法能够在一次迭代过程中并行地估计出多维广义特征向量. 平稳点分析表明: 当且仅当算法中状态矩阵等于所需的广义特征向量时, 算法达到收敛状态. 通过对比相邻时刻的状态矩阵模值证明了所提算法的自稳定特性. 所提算法参数选取简单, 实际实施较为容易. 数值仿真和实例应用进一步验证了算法的并行性、自稳定性和实用性.
• 图  1  所提算法方向余弦曲线

Fig.  1  The DC curves of the proposed algorithm

图  2  GDM算法方向余弦曲线

Fig.  2  The DC curves of the GDM algorithm

图  3  列向量模值曲线

Fig.  3  The norm curves of the column vectors

图  4  不同列向量内积关系曲线

Fig.  4  The inner product curves of different column vectors

图  5  不同对角矩阵下状态矩阵模值曲线

Fig.  5  The norm curves of the state matrix with different diagonal matrices

图  6  源信号波形

Fig.  6  The waveform of source signals

图  7  观测信号曲线

Fig.  7  The waveform of observed signals

图  8  分离信号曲线

Fig.  8  The waveform of separated signals

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##### 计量
• 文章访问数:  107
• HTML全文浏览量:  12
• 被引次数: 0
##### 出版历程
• 收稿日期:  2020-06-10
• 修回日期:  2020-09-26
• 网络出版日期:  2020-12-17

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