Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Extraction of Specific Signals with Temporal Structure
Neural Computation
Blind source separation based on constant modulus criterion and signal mutual information
Computers and Electrical Engineering
An improved method for independent component analysis with reference
Digital Signal Processing
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Nonlinear blind source separation using kernels
IEEE Transactions on Neural Networks
Approach and applications of constrained ICA
IEEE Transactions on Neural Networks
A New Constrained Independent Component Analysis Method
IEEE Transactions on Neural Networks
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Constrained independent component analysis (cICA) is an important technique which can extract the desired sources from the mixtures. The post-nonlinear (PNL) mixture model is more realistic and accurate than the linear instantaneous mixture model in many practical situations. In this paper, we address the problem of extracting the desired source as the first output from the PNL mixture. The prior knowledge about the desired source, such as its rough template (reference), is assumed to be available. Two approaches of extracting PNL signal with reference are discussed. Then a novel algorithm which alternately optimizes the contrast function and the closeness measure between the estimated output and the reference signal is proposed. The inverse of the unknown nonlinear function in the PNL mixture model is approximated by the multi-layer perception (MLP) network. The correctness and validity of the proposed algorithm are demonstrated by our experiment results.