Hinging hyperplane based regression tree identified by fuzzy clustering and its application

Tamás Kenesei, Janos Abonyi

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Hierarchical fuzzy modeling techniques have great advantage since model accuracy and complexity can be easily controlled thanks to the transparent model structures. A novel tool for regression tree identification is proposed based on the synergistic combination of fuzzy c-regression clustering and the concept of hierarchical modeling. In a special case (c = 2), fuzzy c-regression clustering can be used for identification of hinging hyperplane models. The proposed method recursively identifies a hinging hyperplane model that contains two linear submodels by partitioning operating region of one local linear model resulting a binary regression tree. Novel measures of model performance and complexity are developed to support the analysis and building of the proposed special model structure. Effectiveness of proposed model is demonstrated by benchmark regression datasets. Examples also demonstrate that the proposed model can effectively represent nonlinear dynamical systems. Thanks to the piecewise linear model structure the resulted regression tree can be easily utilized in model predictive control. A detailed application example related to the model predictive control of a water heater demonstrate that the proposed framework can be effectively used in modeling and control of dynamical systems.

Original languageEnglish
Pages (from-to)782-792
Number of pages11
JournalApplied Soft Computing Journal
Volume13
Issue number2
DOIs
Publication statusPublished - Jan 1 2013

Keywords

  • Fuzzy-c regression clustering
  • Hinging hyperplanes
  • Model predictive control
  • Non-linear regression
  • Piecewise models
  • Regression trees

ASJC Scopus subject areas

  • Software

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