A Comparative Analysis of Neuro-fuzzy and Grammatical Evolution Models for Simulating Field-Effect Transistors

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

@InProceedings{conf/csie/KaurB09,
  title =        "A Comparative Analysis of Neuro-fuzzy and Grammatical
                 Evolution Models for Simulating Field-Effect
                 Transistors",
  author =       "Devinder Kaur and Dustin Baumgartner",
  booktitle =    "World Congress on Computer Science and Information
                 Engineering, CSIE 2009, 2009 WRI",
  year =         "2009",
  editor =       "Mark Burgin and Masud H. Chowdhury and Chan H. Ham and 
                 Simone A. Ludwig and Weilian Su and Sumanth Yenduri",
  pages =        "179--183",
  address =      "Los Angeles, California, USA",
  month =        mar # " 31-" # apr # " 2",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Neuro Fuzzy Inference System, Field Effect
                 Transistor Modeling",
  bibdate =      "2010-01-13",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/csie/csie2009-5.html#KaurB09",
  isbn13 =       "978-0-7695-3507-4",
  DOI =          "doi:10.1109/CSIE.2009.720",
  abstract =     "In this paper we have developed fuzzy inference system
                 models for a field-effect transistor. The hope is to
                 see if such techniques can be used for inventing future
                 semiconductor based devices. Three modeling techniques
                 have been used. Neuro Fuzzy based on grid partitioning
                 and Neuro Fuzzy based on cluster partitioning create
                 Sugeno Fuzzy Inference Systems, which are trained with
                 a neural network back propagation method. The third
                 modeling technique is based on Grammatical Evolution,
                 where a grammar template in the form of rules is
                 evolved using genetic algorithms based evolutionary
                 techniques. This grammar template is based on the
                 Mamdani Fuzzy Inference System. Experimental results
                 indicate that all models produce acceptable levels of
                 performance, some even have an error rate that is
                 nearly negligible.",
}

Genetic Programming entries for Devinder Kaur Dustin Baumgartner

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