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Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion

机译：基于偏置术语的神经网络的盲源分离方法和最大似然估计标准

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摘要

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.
机译：收敛速度和稳态源分离性能对于实现盲源分离方法的工程应用至关重要。盲源分离算法的损耗函数的修改与算法的优化，从神经网络（NNS）的角度提高其性能（NNS）是一种新颖的概念。本文提出了一种盲源分离方法，组合最大似然估计标准和具有偏置项的NN。该方法为重量和偏置添加了L2正则化术语，以提高稳态性能，并设计一种具有双加速策略的新颖优化算法，以提高算法的收敛速度。建议优化算法的双加速策略平滑并加速了参数空间中最初的陡峭慢梯度下降。与竞争算法相比，该策略将算法的收敛速度提高了四次，稳态性能指数提高了96％。此外，为了更全面地验证算法的源分离性能，使用现有知识和未经事先知识的测量数据的模拟数据用于验证分离性能。基于测量数据的仿真结果和验证结果表明，新算法不仅具有比传统算法更好的收敛性和稳态性能，但它也更适合工程应用。

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