论文标题
使用具有本地学习规则的神经网络的盲目源分离
Blind Bounded Source Separation Using Neural Networks with Local Learning Rules
论文作者
论文摘要
自然和工程信号处理系统遇到的一个重要问题是盲源分离。在许多问题的情况下，即使特定的约束可能不知道，这些来源也受其性质的界限，并且被称为如此。为了将这些有界源与其混合物分开，我们提出了一个新的优化问题，有限的相似性匹配（BSM）。自适应BSM算法的原则推导导致具有剪接非线性的复发性神经网络。该网络通过本地学习规则进行调整，满足了神经形态硬件中生物学上的合理性和可实现性的重要限制。
An important problem encountered by both natural and engineered signal processing systems is blind source separation. In many instances of the problem, the sources are bounded by their nature and known to be so, even though the particular bound may not be known. To separate such bounded sources from their mixtures, we propose a new optimization problem, Bounded Similarity Matching (BSM). A principled derivation of an adaptive BSM algorithm leads to a recurrent neural network with a clipping nonlinearity. The network adapts by local learning rules, satisfying an important constraint for both biological plausibility and implementability in neuromorphic hardware.
