By Topic

Continuous time recurrent neural networks: a paradigm for evolvable analog controller circuits

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)
Gallacher, J.C. ; Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA ; Fiore, J.M.

This paper argues that Continuous Time Recurrent Neural Networks (CTRNNs) provide a particularly attractive paradigm under which to evolve analog electrical circuits for use as device controllers. It will make these arguments both by appeal to existing literature and by the example of a successful project in the control of an autonomous robot. The paper will conclude with a discussion of future work and goals

Published in:

National Aerospace and Electronics Conference, 2000. NAECON 2000. Proceedings of the IEEE 2000

Date of Conference:

2000