TY - JOUR

T1 - NNARX networks on didactic level system identification

AU - Santos Neto, A. F.

AU - Santos, M. F.

AU - Santiago, A. C.

AU - Vidal, V. F.

AU - Mercorelli, Paolo

PY - 2020

Y1 - 2020

N2 - This work has as main objective to propose the identification of a small scale non-linear system through the Neural Network AutoRegressive with eXternal input. The use of this network requires an adequate methodol-ogy for its configuration and, consequently, a good training set. Then, it is proposed that the main definitions of the network parameters be obtained through the analysis of nonintrusive performance indices. Additionally, using a database based on the system’s response, excited by the Pseudo-Random Binary Sequence signal. The method-ology 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 a nonlinear water tank level (Case 02). The results of the identified models were able to represent the system dynamics with high fidelity, presenting an average identification error of less than 0.14 and 0.34% for Case 1 and 2, respectively. Also, it is observed that the learning and generalization evidence could represent the process intrinsic nonlinearities satisfactorily. Besides, it will be possible to find the potentiality and usefulness of the developed network in nonlinear system identification.

AB - This work has as main objective to propose the identification of a small scale non-linear system through the Neural Network AutoRegressive with eXternal input. The use of this network requires an adequate methodol-ogy for its configuration and, consequently, a good training set. Then, it is proposed that the main definitions of the network parameters be obtained through the analysis of nonintrusive performance indices. Additionally, using a database based on the system’s response, excited by the Pseudo-Random Binary Sequence signal. The method-ology 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 a nonlinear water tank level (Case 02). The results of the identified models were able to represent the system dynamics with high fidelity, presenting an average identification error of less than 0.14 and 0.34% for Case 1 and 2, respectively. Also, it is observed that the learning and generalization evidence could represent the process intrinsic nonlinearities satisfactorily. Besides, it will be possible to find the potentiality and usefulness of the developed network in nonlinear system identification.

KW - Level system

KW - NNARX

KW - System identification

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85084839177&partnerID=8YFLogxK

UR - https://wseas.org/wseas/cms.action?id=23195

U2 - 10.37394/23203.2020.15.19

DO - 10.37394/23203.2020.15.19

M3 - Journal articles

AN - SCOPUS:85084839177

VL - 15

SP - 184

EP - 190

JO - WSEAS Transactions on Systems and Control

JF - WSEAS Transactions on Systems and Control

SN - 1991-8763

M1 - 19

ER -