Case Study: Application of NNARX Networks in the Identification of a Small-Scale Level System
Research output: Contributions to collected editions/works › Conference contribution › peer-review
Standard
2019 3rd European Conference on Electrical Engineering and Computer Science, EECS 2019: Athens, Greece 28-30 December 2019, Proceedings . Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 2019. p. 15-18 9257574.
Research output: Contributions to collected editions/works › Conference contribution › peer-review
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - GEN
T1 - Case Study
T2 - 3rd European Conference on Electrical Engineering and Computer Science - EECS 2019
AU - Santos Neto, Accacio Ferreira dos
AU - Santos, Murillo Ferreira dos
AU - Santiago, Augusto Cerqueira
AU - Vidal, Vinícius Ferreira
AU - Mercorelli, Paolo
N1 - The conference was held in December 2019 and the paper appeared in IEEEXplore and in Scopus in December 2020.
PY - 2019
Y1 - 2019
N2 - This work proposes the identification study of a small scale nonlinear system through Neural Network AutoRegressive with eXternal input (NNARX). The use of the NNARX network requires a proper methodology for its configuration and a good training set. This work proposes the main parameter definition of the network through the analysis of non-intrusive performance indexes with the support of a database based on the system response, considering a Pseudo-Random Binary Sequence (PRBS) excitation. The methodology will be applied in two specific open-loop identification situations: Numerical simulation of a fourth order polynomial system (Case 01), and an experimental system that controls the water tank level with nonlinear characteristics (Case 02). The results of the identified models by the developed NNARX network were able to represent the system dynamics with high fidelity, presenting an average identification error of less than 0.14% (Case 01) and 0.34% (Case 02). It was also observed the learning and generalization evidence that could represent the process intrinsic nonlinearities satisfactorily. Besides, it will be possible to find the potentiality and usefulness of the NNARX network in non-linear system identification. Besides, it will be possible to find the potentiality and usefulness of the NNARX network in nonlinear system identification.
AB - This work proposes the identification study of a small scale nonlinear system through Neural Network AutoRegressive with eXternal input (NNARX). The use of the NNARX network requires a proper methodology for its configuration and a good training set. This work proposes the main parameter definition of the network through the analysis of non-intrusive performance indexes with the support of a database based on the system response, considering a Pseudo-Random Binary Sequence (PRBS) excitation. The methodology will be applied in two specific open-loop identification situations: Numerical simulation of a fourth order polynomial system (Case 01), and an experimental system that controls the water tank level with nonlinear characteristics (Case 02). The results of the identified models by the developed NNARX network were able to represent the system dynamics with high fidelity, presenting an average identification error of less than 0.14% (Case 01) and 0.34% (Case 02). It was also observed the learning and generalization evidence that could represent the process intrinsic nonlinearities satisfactorily. Besides, it will be possible to find the potentiality and usefulness of the NNARX network in non-linear system identification. Besides, it will be possible to find the potentiality and usefulness of the NNARX network in nonlinear system identification.
KW - Level System
KW - NNARX networks
KW - System Identification
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85097936758&partnerID=8YFLogxK
U2 - 10.1109/EECS49779.2019.00016
DO - 10.1109/EECS49779.2019.00016
M3 - Conference contribution
AN - SCOPUS:85097936758
SN - 978-1-7281-6110-5
SP - 15
EP - 18
BT - 2019 3rd European Conference on Electrical Engineering and Computer Science, EECS 2019
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
Y2 - 28 December 2019 through 30 December 2019
ER -