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Perturbation signal design for neural network based identification of multivariable nonlinear systems
Author(s) -
Kulkarni Pankaj S.,
Gudi Ravindra D.
Publication year - 2002
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450800115
Subject(s) - multivariable calculus , control theory (sociology) , nonlinear system , perturbation (astronomy) , artificial neural network , computer science , signal (programming language) , control engineering , system identification , identification (biology) , engineering , control (management) , artificial intelligence , physics , data modeling , botany , quantum mechanics , biology , programming language , database
The paper focuses on issues in experimental design for identification of nonlinear multivariable systems. Perturbation signal design is analyzed for a hybrid model structure consisting of linear and neural network structures. Input signals, designed to minimize the effects of nonlinearities during the linear model identification for the multivariable case, have been proposed and its properties have been theoretically established. The superiority of the proposed perturbation signal and the hybrid model has been demonstrated through extensive cross validations. The utility of the obtained models for control has also been proved through a case study involving MPC of a nonlinear multivariable neutralization plant.