117 research outputs found
Enhanced Nonlinear System Identification by Interpolating Low-Rank Tensors
Function approximation from input and output data is one of the most
investigated problems in signal processing. This problem has been tackled with
various signal processing and machine learning methods. Although tensors have a
rich history upon numerous disciplines, tensor-based estimation has recently
become of particular interest in system identification. In this paper we focus
on the problem of adaptive nonlinear system identification solved with
interpolated tensor methods. We introduce three novel approaches where we
combine the existing tensor-based estimation techniques with multidimensional
linear interpolation. To keep the reduced complexity, we stick to the concept
where the algorithms employ a Wiener or Hammerstein structure and the tensors
are combined with the well-known LMS algorithm. The update of the tensor is
based on a stochastic gradient decent concept. Moreover, an appropriate step
size normalization for the update of the tensors and the LMS supports the
convergence. Finally, in several experiments we show that the proposed
algorithms almost always clearly outperform the state-of-the-art methods with
lower or comparable complexity.Comment: 12 pages, 4 figures, 3 table
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