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

► Hinging hyperplane identification with constrained fuzzy c-regression method is presented. ► Tree based representation of hinging hyperplane models is described. ► Proposed algorithm is used for model predictive control of a cartridge water heater. Hierarchical fuzzy modeling techniques have great...

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Bibliographic Details
Published in:Applied soft computing Vol. 13; no. 2; pp. 782 - 792
Main Authors: Kenesei, Tamás, Abonyi, János
Format: Journal Article
Language:English
Published: Elsevier B.V 01.02.2013
Subjects:
Fuzzy-c regression clustering
Hinging hyperplanes
Model predictive control
Non-linear regression
Piecewise models
Regression trees
Model predictive control
Non-linear regression
Hinging hyperplanes
Piecewise models
Regression trees
Fuzzy-c regression clustering
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:► Hinging hyperplane identification with constrained fuzzy c-regression method is presented. ► Tree based representation of hinging hyperplane models is described. ► Proposed algorithm is used for model predictive control of a cartridge water heater. 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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2012.09.027