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Genetic programming of an artificial neural network for robust control of a 2-D path following robot

Roy, Anthony M. and Antonsson, Erik K. and Shapiro, Andrew A. (2009) Genetic programming of an artificial neural network for robust control of a 2-D path following robot. In: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASME , pp. 799-805. ISBN 978-0-7918-4325-3. https://resolver.caltech.edu/CaltechAUTHORS:20100618-141102014

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Abstract

Genetic Programs that have phenotypes created by the application of genotypes comprising rules are robust and highly scalable. Such encodings are useful for complex applications such as controller design. This paper outlines an evolutionary algorithm capable of creating a controller for 2 DOF, path following robot. The controllers are embodied by Artificial Neural Networks capable of full functionality despite multiple failures.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1115/DETC2008-50137DOIUNSPECIFIED
Additional Information:© 2008 ASME. Part of the research described in this paper was sponsored by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
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Funding AgencyGrant Number
JPL/CaltechUNSPECIFIED
Record Number:CaltechAUTHORS:20100618-141102014
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20100618-141102014
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:18737
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Jul 2010 23:19
Last Modified:03 Oct 2019 01:47

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