<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Verma, Devendra</style></author><author><style face="normal" font="default" size="100%">Goel, Purva</style></author><author><style face="normal" font="default" size="100%">Patil-Shinde, Veena</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use genetic programming for selecting predictor variables and modeling in process identification</style></title><secondary-title><style face="normal" font="default" size="100%">2016 Indian Control Conference (ICC)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dynamic model</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">predictor variable</style></keyword><keyword><style  face="normal" font="default" size="100%">process identification</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE Control Syst Soc; Honeywell; Mathworks; ABB R &amp; D Div; GE; Mahindra Ecole Centrale</style></publisher><pub-location><style face="normal" font="default" size="100%">345 E 47th ST, New York, NY 10017 USA</style></pub-location><pages><style face="normal" font="default" size="100%">230-237</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4673-7993-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Availability of an accurate and robust dynamic model is essential for implementing the model dependent process control. When first principles based modeling becomes difficult, tedious and/or costly, a dynamic model in the black-box form is obtained (process identification) by using the measured input-output process data. Such a dynamic model frequently contains a number of time delayed inputs and outputs as predictor variables. The determination of the specific predictor variables is usually done via a trial and error approach that requires an extensive computational effort. The computational intelligence (CI) based data-driven modeling technique, namely, genetic programming (GP) can search and optimize both the structure and parameters of a linear/nonlinear dynamic process model. It is also capable of choosing those predictor variables that significantly influence the model output. Thus usage of GP for process identification helps in avoiding the extensive time and efforts involved in the selection of the time delayed input-output variables. This advantageous GP feature has been illustrated in this study by conducting process identification of two chemical engineering systems. The results of the GP-based identification when compared with those obtained using the transfer function based identification clearly indicates the outperformance by the former method.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">2nd Indian Control Conference (ICC), Indian Inst Technol, Hyderabad, INDIA, JAN 04-06, 2016</style></notes></record></records></xml>