We present a new kernel-based algorithm for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification. The presented method reorganizes the typical single-layer kernel-based model into a deep hierarchical structure, such that the weights of a kernel model over each dimension are modeled over its adjacent dimension. We show that modeling weights in the suggested structure leads to significant computational speedup and improved modeling accuracy.

Original languageEnglish (US)
Pages (from-to)2515-2530
Number of pages16
JournalNonlinear Dynamics
Issue number3
StatePublished - May 2021


  • Deep hierarchical structure
  • Kernel recursive least square
  • Multidimensional dataset

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering


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