Abstract
This paper concerns our general recurrent neural network structures for nonlinear blind source separation, especially suited to polynomial mixtures. We here focus on linear-quadratic mixtures. We introduce an extended structure, with additional free parameters as compared to the structure that we previously proposed. We derive the equilibrium points of our new structure, thus showing that it has no spurious fixed points. We analyze its stability in detail and propose a practical procedure for selecting its free parameters, so as to guarantee the stability of a separating point. We thus solve the stability issue of our previous structure. Numerical results illustrate the effectiveness of this approach.
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Deville, Y., Hosseini, S. (2007). Stable Higher-Order Recurrent Neural Network Structures for Nonlinear Blind Source Separation. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_21
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DOI: https://doi.org/10.1007/978-3-540-74494-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
Online ISBN: 978-3-540-74494-8
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