By Topic

Applications of GA-based optimization of neural network connection topology

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
$31 $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)
Smuda, E. ; Dept. of Aerosp. Eng., Alabama Univ., Tuscaloosa, AL, USA ; KrishnaKumar, K.

A genetic algorithm (GA) is used to explore the connection space of an artificial neural network (ANN) with the objective of finding a sparsely connected network that yields the same accuracy as a fully connected network. Such sparsity is desired as it improves the generalization capabilities of the mapping. The ANN with the GA-chosen set of connections is then trained using a supervised mode of learning known as backpropagation error. Using this technique, three different applications are analyzed

Published in:

System Theory, 1993. Proceedings SSST '93., Twenty-Fifth Southeastern Symposium on

Date of Conference:

7-9 Mar 1993