Paper Info
Title
Two-Dimensional (2D) Particle Swarms for Structure Selection of Nonlinear Systems.
Abstract
The present study proposes a new structure selection approach for non-linear system identification based on Two-Dimensional particle swarms (2D-UPSO). The 2D learning framework essentially extends the learning dimension of the conventional particle swarms and explicitly incorporates information about the cardinality (i.e., number of terms) into the search process. This property of the 2D-UPSO is exploited to determine the correct structure of the non-linear systems. The efficacy of the proposed approach is demonstrated by considering several simulated benchmark nonlinear systems in discrete and continuous domains. In addition, the proposed approach is applied to identify a parsimonious structure from practical non-linear wave-force data. The results of the comparative investigation with four meta-heuristic algorithms and classical orthogonal forward regression methods illustrate that the proposed 2D-UPSO can successfully detect the correct structure of the non-linear systems.
Year
DOI
Venue
2018
10.1016/j.neucom.2019.07.071
Neurocomputing
Keywords
Field
DocType
Nonlinear systems,NARX model,Particle swarm optimization,Structure selection,System identification
Mathematical optimization,Nonlinear system,Binary particle swarm optimization,Cardinality,System identification,Mathematics,Genetic algorithm,Particle,Forward regression
Journal
Volume
ISSN
Citations 
367
0925-2312
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Faizal M. F. Hafiz1113.29
Akshya K. Swain2113.33
Eduardo M. A. M. Mendes3388.93