Reduced state nonlinear prediction with state reduction

B. Mulgrew, K. Nisbet, S. McLaughlin

Research output: Contribution to conferencePaper


The signal subspace technique for state reduction in nonlinear Volterra series (VS) and radial basis function (RBF) predictors are examined. The concept of applying signal subspace techniques to nonlinear prediction problems was first presented by Mulgrew et al. (see IEE Colloquium on Adaptive Filters, 1991). Since then, two alternative approaches (the indirect method and the direct method) have been developed. Results are presented which demonstrate the effectiveness of these techniques when applied to the prediction of chaotic time series
Original languageEnglish
Publication statusPublished - Feb 1993
Externally publishedYes
EventIEE Colloquium New Directions in Adaptive Signal Processing - London
Duration: 16 Feb 199316 Feb 1993


ConferenceIEE Colloquium New Directions in Adaptive Signal Processing


  • adaptive nonlinear prediction ,
  • state reduction
  • signal subspace technique
  • nonlinear Volterra series
  • radial basis function
  • chaotic time series


Dive into the research topics of 'Reduced state nonlinear prediction with state reduction'. Together they form a unique fingerprint.

Cite this