Open AccessBlack-box modeling of nonlinear system using evolutionary neural NARX model
Author(s) -
Nguyễn Ngọc Sơn,
Nguyễn Duy Khánh,
Tran Minh Chinh
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i3.pp1861-1870
Subject(s) - black box , nonlinear autoregressive exogenous model , artificial neural network , nonlinear system , computer science , control theory (sociology) , system dynamics , differential evolution , neuro fuzzy , evolutionary algorithm , mimo , fuzzy logic , control engineering , artificial intelligence , fuzzy control system , control (management) , engineering , physics , quantum mechanics , computer network , channel (broadcasting)
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.