Evolving fuzzy inferential sensors for process industry

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Plamen Angelov and Arthur Kordon and Xiaowei Zhou",
  title =        "Evolving fuzzy inferential sensors for process
  booktitle =    "3rd International Workshop on Genetic and Evolving
                 Fuzzy Systems, GEFS 2008",
  year =         "2008",
  month =        "4-7 " # mar,
  address =      "Witten-Boommerholz, Germany",
  pages =        "41--46",
  keywords =     "genetic algorithms, genetic programming, Dow Chemical
                 Company, Takagi-Sugeno-fuzzy system, fuzzy inferential
                 sensor, multi-objective genetic-programming-based
                 optimization, on-line input selection techniques,
                 on-line learning algorithm, process industry,
                 self-tuning inferential soft sensor, chemical industry,
                 fuzzy set theory, fuzzy systems, sensors",
  DOI =          "doi:10.1109/GEFS.2008.4484565",
  abstract =     "This paper describes an approach to design
                 self-developing and self-tuning inferential soft
                 sensors applicable to process industries. The proposal
                 is for a Takagi-Sugeno-fuzzy system framework that has
                 evolving (open structure) architecture, and an on-line
                 (possibly real-time) learning algorithm. The proposed
                 methodology is novel and it addresses the problems of
                 self-development and self-calibration caused by drift
                 in the data patterns due to changes in the operating
                 regimes, catalysts aging, industrial equipment wearing,
                 contamination etc. The proposed computational technique
                 is data-driven and parameter-free (it only requires a
                 couple of parameters with clear meaning and suggested
                 values). In this paper a case study of four problems of
                 estimation of chemical properties is considered,
                 however, the methodology has a much wider validity. The
                 optimal inputs to the proposed evolving inferential
                 sensor are determined a priori and off-line using a
                 multi-objective genetic-programming-based optimization.
                 Different on-line input selection techniques are under
                 development. The methodology is validated on real data
                 provided by the Dow Chemical Company, USA.",
  notes =        "Also known as \cite{4484565}",

Genetic Programming entries for Plamen Angelov Arthur K Kordon Xiaowei Zhou