Use genetic programming for selecting predictor variables and modeling in process identification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Verma:2016:ICC,
  author =       "Devendra Verma and Purva Goel and 
                 Veena Patil-Shinde and Sanjeev S. Tambe",
  booktitle =    "2016 Indian Control Conference (ICC)",
  title =        "Use genetic programming for selecting predictor
                 variables and modeling in process identification",
  year =         "2016",
  pages =        "230--237",
  abstract =     "Availability of an accurate and robust dynamic model
                 is essential for implementing the model dependent
                 process control. When first principles based modelling
                 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 modelling technique, namely, genetic
                 programming (GP) can search and optimise 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 out
                 performance by the former method.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/INDIANCC.2016.7441133",
  month =        jan,
  notes =        "Chemical Engineering and Process Development Division,
                 CSIR-National Chemical Laboratory, Pune, India

                 Also known as \cite{7441133}",
}

Genetic Programming entries for Devendra Verma Purva Goel Veena Patil-Shinde Sanjeev S Tambe

Citations