Liu Jian-Qiang, Feng Da-Zheng, Zhang Wei-Wei
National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Neural Comput. 2009 Mar;21(3):872-89. doi: 10.1162/neco.2008.07-07-562.
We propose an adaptive improved natural gradient algorithm for blind separation of independent sources. First, inspired by the well-known backpropagation algorithm, we incorporate a momentum term into the natural gradient learning process to accelerate the convergence rate and improve the stability. Then an estimation function for the adaptation of the separation model is obtained to adaptively control a step-size parameter and a momentum factor. The proposed natural gradient algorithm with variable step-size parameter and variable momentum factor is therefore particularly well suited to blind source separation in a time-varying environment, such as an abruptly changing mixing matrix or signal power. The expected improvement in the convergence speed, stability, and tracking ability of the proposed algorithm is demonstrated by extensive simulation results in both time-invariant and time-varying environments. The ability of the proposed algorithm to separate extremely weak or badly scaled sources is also verified. In addition, simulation results show that the proposed algorithm is suitable for separating mixtures of many sources (e.g., the number of sources is 10) in the complete case.
我们提出一种用于独立源盲分离的自适应改进自然梯度算法。首先，受著名的反向传播算法启发，我们在自然梯度学习过程中引入一个动量项，以加快收敛速度并提高稳定性。然后，获得一个用于分离模型自适应的估计函数，以自适应地控制步长参数和动量因子。因此，所提出的具有可变步长参数和可变动量因子的自然梯度算法特别适用于时变环境中的盲源分离，例如突然变化的混合矩阵或信号功率。在时不变和时变环境中的大量仿真结果表明了所提算法在收敛速度、稳定性和跟踪能力方面预期的改进。所提算法分离极弱或尺度不佳源的能力也得到了验证。此外，仿真结果表明所提算法适用于完全情况下分离多个源的混合信号（例如，源的数量为10）。
