An adaptable Boolean net trainable to control a computing robot
作者:
F. E. Lauria,
R. Prevete,
M. Milo,
S. Visco,
期刊:
AIP Conference Proceedings
(AIP Available online 1999)
卷期:
Volume 465,
issue 1
页码: 169-182
ISSN:0094-243X
年代: 1999
DOI:10.1063/1.58263
出版商: AIP
数据来源: AIP
摘要:
We discuss a method to implement in a Boolean neural network a Hebbian rule so to obtain an adaptable universal control system. We start by presenting both the Boolean neural net and the Hebbian rule we have considered. Then we discuss, first, the problems arising when the latter is naively implemented in a Boolean neural net, second, the method consenting us to overcome them and the ensuing adaptable Boolean neural net paradigm. Next, we present the adaptable Boolean neural net as an intelligent control system, actually controlling a writing robot, and discuss how to train it in the execution of the elementary arithmetic operations on operands represented by numerals with an arbitrary number of digits. ©1999 American Institute of Physics.
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