Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion.
Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion.
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DOI：
10.3390/s21030973
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发表时间：
2021-02-01
期刊：
影响因子：
--
通讯作者：
Zhang L
中科院分区：
文献类型：
--
作者：
Liu S;Wang B;Zhang L
盲源分离方法的收敛速度和稳态分离性能是其工程应用的关键。从神经网络的角度对盲源分离算法的损失函数进行修正和优化以提高其性能是一个新的概念。本文提出了一种将最大似然估计准则与带偏项的神经网络相结合的盲源分离方法。该方法通过在损失函数中加入L2正则化项来提高权值和偏差的稳态性能，并设计了一种新的优化算法，采用双重加速策略来提高算法的收敛速度。所提出的优化算法的双重加速策略平滑和加速了原本陡峭的，缓慢的梯度下降的参数空间。与同类算法相比，该策略使算法的收敛速度提高了4倍，稳态性能指标提高了96%。此外，为了更全面地验证算法的源分离性能，采用了有先验知识的仿真数据和无先验知识的实测数据对分离性能进行了验证。仿真结果和实测数据验证结果表明，该算法不仅具有更好的收敛性和稳态性能，而且更适合工程应用。
Convergence speed and steady-state source separation performance are crucial for enable engineering applications of blind source separation methods. The modification of the loss function of the blind source separation algorithm and optimization of the algorithm to improve its performance from the perspective of neural networks (NNs) is a novel concept. In this paper, a blind source separation method, combining the maximum likelihood estimation criterion and an NN with a bias term, is proposed. The method adds L2 regularization terms for weights and biases to the loss function to improve the steady-state performance and designs a novel optimization algorithm with a dual acceleration strategy to improve the convergence speed of the algorithm. The dual acceleration strategy of the proposed optimization algorithm smooths and speeds up the originally steep, slow gradient descent in the parameter space. Compared with competing algorithms, this strategy improves the convergence speed of the algorithm by four times and the steady-state performance index by 96%. In addition, to verify the source separation performance of the algorithm more comprehensively, the simulation data with prior knowledge and the measured data without prior knowledge are used to verify the separation performance. Both simulation results and validation results based on measured data indicate that the new algorithm not only has better convergence and steady-state performance than conventional algorithms, but it is also more suitable for engineering applications.
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影响因子：
7.8
作者：
Hyvärinen, A;Oja, E
通讯作者：
Oja, E
影响因子：
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作者：
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影响因子：
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DOI：
10.1016/j.engappai.2005.09.006
发表时间：
2006-04-01
影响因子：
8
作者：
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通讯作者：
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影响因子：
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