By Topic

A Reconfigurable Analog Neural Network for Evolvable Hardware Applications: Intrinsic Evolution and Extrinsic Verification

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

3 Author(s)
Boddhu, S.K. ; Wright State Univ., Dayton ; Gallagher, J.C. ; Vigraham, S.

Continuous time recurrent neural networks (CTRNN) have been proposed for use as reconfigurable hardware for evolvable hardware (EH) applications. Our previous work demonstrated a fully programmable hardware CTRNN using off-the-shelf components and provided verification of its utility in extrinsic EH. However, applicability for intrinsic usage was not studied. This work addresses that unanswered issue and demonstrates that configurations evolved in the hardware are behaviorally equivalent to simplified state equation models. Further, this work also provides strong similarity metrics to compare the hardware's performance with software simulated CTRNN models.

Published in:

Evolutionary Computation, 2006. CEC 2006. IEEE Congress on

Date of Conference:

0-0 0