By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kaur, D. ; Univ. of Toledo, Toledo, OH, USA ; Baumgartner, D.

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.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:5 )

Date of Conference:

March 31 2009-April 2 2009