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Adaptive hinging hyperplanes

  • Jun Xu
  • , Xiaolin Huang
  • , Shuning Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The model of adaptive hinging hyperplanes (AHH) is proposed in this paper for black-box modeling. It is based on Multivariate Adaptive Regression Splines (MARS) and Generalized Hinging Hyperplanes (GHH) and shares attractive properties of the two. By making a modification to the basis function of MARS, AHH shows linear property in each subarea. It is proved that AHH model is identical to a special case of the Generalized Hinging Hyperplanes (GHH) model, which has a universal representation capability for continuous piecewise linear functions. AHH algorithm is developed similar to MARS algorithm. It is adaptive and can be executed quickly, hence has power and flexibility to model unknown relationships. In addition, due to the piecewise-linear property, AHH is preferred to MARS when modeling high-dimensional dynamic systems, especially when the sample size is small and under noise conditions.

Original languageEnglish
Title of host publicationProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Edition1 PART 1
DOIs
StatePublished - 2008
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 6 Jul 200811 Jul 2008

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
Volume17
ISSN (Print)1474-6670

Conference

Conference17th World Congress, International Federation of Automatic Control, IFAC
Country/TerritoryKorea, Republic of
CitySeoul
Period6/07/0811/07/08

Keywords

  • Nonlinear system identification

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