TY - JOUR
T1 - A Tree Adjoining Grammar representation for models of stochastic dynamical systems
AU - Khandelwal, Dhruv
AU - Schoukens, Maarten
AU - Tóth, Roland
PY - 2020/9
Y1 - 2020/9
N2 - Model structure and complexity selection remains a challenging problem in system identification, especially for parametric non-linear models. Many Evolutionary Algorithm (EA) based methods have been proposed in the literature for estimating model structure and complexity. In most cases, the proposed methods are devised for estimating structure and complexity within a specified model class and hence these methods do not extend to other model structures without significant changes. In this paper, we propose a Tree Adjoining Grammar (TAG) for stochastic parametric models. TAGs can be used to generate models in an EA framework while imposing desirable structural constraints and incorporating prior knowledge. In this paper, we propose a TAG that can systematically generate models ranging from FIRs to polynomial NARMAX models. Furthermore, we demonstrate that TAGs can be easily extended to more general model classes, such as the non-linear Box–Jenkins model class, enabling the realization of flexible and automatic model structure and complexity selection via EA.
AB - Model structure and complexity selection remains a challenging problem in system identification, especially for parametric non-linear models. Many Evolutionary Algorithm (EA) based methods have been proposed in the literature for estimating model structure and complexity. In most cases, the proposed methods are devised for estimating structure and complexity within a specified model class and hence these methods do not extend to other model structures without significant changes. In this paper, we propose a Tree Adjoining Grammar (TAG) for stochastic parametric models. TAGs can be used to generate models in an EA framework while imposing desirable structural constraints and incorporating prior knowledge. In this paper, we propose a TAG that can systematically generate models ranging from FIRs to polynomial NARMAX models. Furthermore, we demonstrate that TAGs can be easily extended to more general model classes, such as the non-linear Box–Jenkins model class, enabling the realization of flexible and automatic model structure and complexity selection via EA.
KW - Evolutionary algorithms
KW - System identification
KW - Tree Adjoining Grammar
UR - http://www.scopus.com/inward/record.url?scp=85087202274&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2020.109099
DO - 10.1016/j.automatica.2020.109099
M3 - Article
AN - SCOPUS:85087202274
VL - 119
JO - Automatica
JF - Automatica
SN - 0005-1098
M1 - 109099
ER -