A Comparative Analysis of Neuro-fuzzy and Grammatical Evolution Models for Simulating Field-Effect Transistors
Created by W.Langdon from
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- @InProceedings{conf/csie/KaurB09,
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title = "A Comparative Analysis of Neuro-fuzzy and Grammatical
Evolution Models for Simulating Field-Effect
Transistors",
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author = "Devinder Kaur and Dustin Baumgartner",
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booktitle = "World Congress on Computer Science and Information
Engineering, CSIE 2009, 2009 WRI",
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year = "2009",
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editor = "Mark Burgin and Masud H. Chowdhury and Chan H. Ham and
Simone A. Ludwig and Weilian Su and Sumanth Yenduri",
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pages = "179--183",
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address = "Los Angeles, California, USA",
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month = mar # " 31-" # apr # " 2",
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publisher = "IEEE Computer Society",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Neuro Fuzzy Inference System, Field Effect
Transistor Modeling",
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bibdate = "2010-01-13",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/csie/csie2009-5.html#KaurB09",
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isbn13 = "978-0-7695-3507-4",
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DOI = "doi:10.1109/CSIE.2009.720",
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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
Citations