Generalized eigenvector algorithm for nonlinear system identification with non-white inputs |
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Authors: | David T Westwick Robert E Kearney |
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Institution: | (1) Department of Biomedical Engineering, Boston University, 44 Cummington Street, 02215 Boston, MA, USA |
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Abstract: | Traditional methods for nonlinear system identification require a white, Gaussian, test input, a restriction that has limited
their usability in many fields. In this study, we address the problem of identifying the dynamics of a nonlinear system when
the input is highly colored—a restriction commonly encountered in the study of physiological systems. An extension of the
parallel cascade method is developed that is optimal in a constrained minimum mean squared error sense and exactly corrects
for the distortion induced by the non-white input spectrum. However, this correction is a deconvolution, which may become
extremely ill-conditioned if the input spectrum depart significantly from whiteness; to confront this, we develop a low-rank
projection operation that stabilizes the deconvolution. The overall algorithm is robust and places few requirements on the
nature of the test input. Practical application of this new method is demonstrated by using it to identify a known analog
nonlinear system from experimental data. |
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Keywords: | Colored-input Generalized eigenvector Matrix square root Pseudo-inverse Deconvolution Volterra kernels Parallel Wiener cascade |
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