A neural net for blind separation of nonstationary signal sources

This paper proposes a neural network that learns to recover the original random signals from their linear mixtures observed by the same number of sensors. The network acquires the function without using any information about the statistical properties of the sources and the coefficients of the linear transformation, except the assumption that the source signals are statistically independent and nonstationary. The learning rule is formulated as a steepest descent minimization of a time-dependent cost function that takes the minimum only when the network outputs are uncorrelated with each other