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

Content Provider	IEEE Xplore Digital Library
Author	Kaur, D. Baumgartner, D.
Copyright Year	2009
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.
Starting Page	179
Ending Page	183
File Size	391807
Page Count	5
File Format	PDF
ISBN	9780769535074
DOI	10.1109/CSIE.2009.720
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Grammatical Evolution Genomics Electrodes Analytical models Field Effect Transistor Modeling Neuro Fuzzy Inference System Production MOSFET circuits Fuzzy neural networks Threshold voltage Bioinformatics FETs Fuzzy systems
Content Type	Text
Resource Type	Article

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