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Descripción
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| Multiple-input multiple-output systems are increasingly important in a great number of fields, as is the case with telecommunications, robotics, biology, neuroscience, etc. In this paper, Volterra models are applied to a class of MIMO nonlinear systems, showing that linearity with respect to the coefficients ensures the availability of a global solution for the identification problem. The applicability of traditional learning algorithms, as Least-Mean-Square (LMS), is conditioned by eigenvalue spread, mainly dominated by nonlinear effects. This convergence issue and others are shown by means of a theoretical treatment and some examples. | |
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Internacional
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Si |
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Nombre congreso
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6th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM'10) |
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Tipo de participación
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960 |
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Lugar del congreso
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Ma'ale Hahamisha (Israel) |
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Revisores
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Si |
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ISBN o ISSN
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978-1-4244-8977-0 |
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DOI
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Fecha inicio congreso
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04/10/2010 |
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Fecha fin congreso
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07/10/2010 |
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Desde la página
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1 |
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Hasta la página
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4 |
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Título de las actas
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Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop |