DocumentCode :
957655
Title :
A simplified neural network solution through problem decomposition: the case of the truck backer-upper
Author :
Jenkins, Robert E. ; Yuhas, Ben P.
Author_Institution :
Appl. Phys. Lab., Johns Hopkins Univ., Baltimore, MD, USA
Volume :
4
Issue :
4
fYear :
1993
fDate :
7/1/1993 12:00:00 AM
Firstpage :
718
Lastpage :
720
Abstract :
D.H. Nguyen and B. Widrow (1990) demonstrated that a feedforward neural network can be trained to steer a tractor-trailer truck to a dock while backing up. The feedforward network they used to control the truck contained 25 hidden units and required extensive training. The authors demonstrate that a very simple solution to the truck backer-upper exists and can be found by decomposing the problem into subtasks. By hard-wiring these control laws into a network, they found a controller with only two hidden units that performs as well as the larger controller trained from scratch. This approach could be used to build up more complex controllers from simple components
Keywords :
feedforward neural nets; learning (artificial intelligence); position control; road vehicles; feedforward neural network; hidden units; problem decomposition; steering control; tractor-trailer truck; Computer aided software engineering; Error correction; Feedforward neural networks; Feedforward systems; Goniometers; Laboratories; Neural networks; Nonlinear control systems; Physics; Wheels;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.238326
Filename :
238326
Link To Document :
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