A NEURAL-NET FOR BLIND SEPARATION OF NONSTATIONARY SIGNALS

This paper proposes a neural network that recovers some original random signals from their linear mixtures observed by the same number of sensors. The network acquires the function with a learning process without using any particular information about the statistical properties of the sources and the coefficients of the linear transformation except the fact that the source signals are statistically independent and nonstationary. The learning rule for the network's parameters is derived from the steepest descent minimization of a time-dependent cost function that takes the minimum only when the network outputs are uncorrelated with each other.