欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
稀疏成分分析是信号处理中解决欠定盲源分离问题的新方法，本文研究了稀疏成分分析中的混合矩阵估计问题，提出了无需预知源个数利用一种相似性函数估计混合矩阵的方法．首先，估计相似性函数中的核参数，使得算法适应不同的稀疏信号．然后，给出了估计混合矩阵的不动点算法．最后，实验结果表明提出的算法通过适当地选取参数，能够准确有效地估计出具有不同源个数的混合矩阵，对不太稀疏的源也有令人满意的结果．
英文摘要：
Sparse component analysis is a new approach to solve the problem of underde- termined blind source separation in signal processing area. In this paper, a clustering method based on similarity function is developed to estimate the mixing matrix in the case of unknowing the number of source signals. Firstly, the kernel parameter of similarity function is estimated so that the proposed algorithm adapts to different kinds of sparse signals. Then, a fixed point algorithm is formulated to estimate the mixing matrix. Finally, the simulations show that through choosing parameters appropriately, our algorithm effectively and accurately estimates the mixing matrix with different numbers of sources. The results for insufficient sparsity sources is also satisfactory.
同期刊论文项目
同项目期刊论文
Consistency and consensus models for group decision-making with uncertain 2-tuple linguistic prefere
Consistency-based algorithms to estimate missing elements for uncertain 2-tuple linguistic preferenc
A VIKOR-based approach to group decision making with uncertain preference ordinals and incomplete we
Similarity measure based on piecewise linear approximation and derivative dynamic time warping for t
期刊信息
位置：