A Novel Genetic Programming Approach for Frequency-dependent Modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Iraj {Rahimi Pordanjani} and 
                 Hooman {Erfanian Mazin} and Wilsun Xu",
  title =        "A Novel Genetic Programming Approach for
                 Frequency-dependent Modeling",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2013",
  volume =       "17",
  number =       "3",
  pages =        "353--367",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Equivalent
                 electric circuit, Frequency-dependent modelling,
                 Frequency-domain response, Rational approximation",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6194300",
  DOI =          "doi:10.1109/TEVC.2012.2197400",
  ISSN =         "1089-778X",
  size =         "15 pages",
  abstract =     "Frequency-dependent modelling of devices and systems
                 is a common practice in several fields such as power
                 systems, microwave systems, and electronic systems. The
                 modelling process usually involves converting the
                 tabulated frequency-response data into a compact
                 equivalent circuit model. The main drawback of the
                 currently existing methods such as vector fitting is
                 that the obtained model is often non-passive, leading
                 to unstable simulations. In order to overcome this
                 problem, this paper proposes a Genetic Programming (GP)
                 approach to generate equivalent circuits with
                 guaranteed passivity. The proposed method starts with a
                 non-optimal initial equivalent circuit. Both the
                 elements and topology of this circuit are then evolved
                 by the proposed GP-based method, and an accurate
                 equivalent circuit is obtained. Key ideas and detailed
                 algorithms are presented in this paper. Finally, the
                 performance of the proposed method is verified by using
                 different case studies.",
  notes =        "TEVC-00352-2010.R2 Also known as \cite{6194300}",

Genetic Programming entries for Iraj Rahimi Pordanjani Hooman Erfanian Mazin Wilsun Xu