Identification of dynamic systems using Piecewise-Affine basis function models

Piecewise-Affine (PWA) Basis Function AutoRegressive eXogenous (BPWARX) models are proposed in this paper for nonlinear black-box identification. A BPWARX model is a weighted sum of PWA Basis (BPWA) functions, which are the minimum or maximum of n + 1 affine functions in n dimensions. Since the BPWA...

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Bibliographic Details
Published inAutomatica (Oxford) Vol. 43; no. 10; pp. 1824 - 1831
Main Authors Wen, Chengtao, Wang, Shuning, Jin, Xuexiang, Ma, Xiaoyan
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.10.2007
Elsevier
Subjects
Applied sciences
Canonical PWA representation
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
Model structures
Modelling and identification
Piecewise-affine systems
PWA Basis function
System identification
Model structures
PWA Basis function
Piecewise-affine systems
System identification
Canonical PWA representation
Systeme affine in control
ARX model
Autoregressive model
Computational complexity
Modeling
Hyperplane
Gaussian process
Function representation
Piecewise linearization
Input output model
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Summary:Piecewise-Affine (PWA) Basis Function AutoRegressive eXogenous (BPWARX) models are proposed in this paper for nonlinear black-box identification. A BPWARX model is a weighted sum of PWA Basis (BPWA) functions, which are the minimum or maximum of n + 1 affine functions in n dimensions. Since the BPWA functions have a universal representation capability for continuous PWA functions, the BPWARX models provide better accuracy than the Hinging Hyperplane ARX (HHARX) models with the same number of parameters, and the same order of computational complexity, when using a modified Gauss–Newton algorithm to build the models from input–output data.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2007.03.003