Information-theoretic approach to blind separation of sources in non-linear mixture
Signal Processing - Special issue on neural networks
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In this paper we present a novel neural network topology capable of separating simultaneous signals transferred through a memoryless non-linear path. The employed neural network is a two-layer perceptron that uses parametric non-linearities in the hidden neurons. The non-linearities are formed using a mixture of sigmoidal non-linear functions and present greater adaptation towards separating complex non-linear mixed signals. Simulation results using complex forms of non-linear mixing functions prove the efficacy of the proposed algorithm when compared to similar networks that use standard nonlinearities, achieving excellent separation performance and faster convergence behavior.