Extraction of Specific Signals with Temporal Structure
Neural Computation
An improved method for independent component analysis with reference
Digital Signal Processing
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Approach and applications of constrained ICA
IEEE Transactions on Neural Networks
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To address the problem of extracting specific signal from the post-nonlinear (PNL) mixture, we propose a novel algorithm based on maximum negentropy. Assume that the prior knowledge of the desired source, such as its rough template referred as the references signal, is available. The closeness measurement between the corresponding estimated output and given reference signal is treated as a constraint and incorporated into the negentropy objective function. Therefore, a constrained optimization problem is formed, which is solved by the augmented Lagrange function method with standard gradient descent learning. The inverse of the unknown nonlinear function in the post-nonlinear (PNL) mixture model is approximated by the multilayer perceptions (MLP) network. Experiments on the synthesis dataset demonstrate the validity of our proposed algorithm.