Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Approach to Nonlinear Blind Source Separation Based on Niche Genetic Algorithm
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Nonlinear blind source separation by spline neural networks
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Blind source separation of a class of nonlinear mixtures
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Post-nonlinear blind source separation using neural networks with sandwiched structure
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nonlinear blind source separation using higher order statistics anda genetic algorithm
IEEE Transactions on Evolutionary Computation
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The nonlinear independent component analysis (NLICA) is an extension of the standard ICA model that does not restrict the mixing system to be linear. Different algorithms have been proposed to solve the NLICA problem, but, as the dimension of the problem increases, most of them present limitations such as poor accuracy and high computational cost. In this work, a novel structural model is proposed for the overdetermined NLICA problem (when there exist more sensors than sources), by adding a signal compaction block to the standard post-nonlinear (PNL) de-mixing model. The proposed methodology proves to be efficient in the feature extraction phase of a challenging high-dimensional online neural discrimination problem.