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Investigators
Z. Obradovic
Problem
Although most of the neural network studies use analog neurons with
continuous monotone increasing transfer functions, the reasons for
using them are not very well founded. The objective of this project
was to study computational ability of more general neural networks
whose transfer functions are not necessarily monotone.
Results
A nonmonotone multi-valued neural network is a nonmonotone extension
of our previously studied multivalued neural network model (obradovic94j),
(obradovic92j), (ngom98) proposed for reasoning about certain aspects
of the behavior of limited precision analog neural networks with
arbitrary continuous transfer functions.
In (obradovic96j), the nonmonotone multi-valued model is compared
to monotone multi-valued and to nonmonotone binary neural networks
and it is shown that the models are essentially equivalent. However,
the savings in time and hardware arising from using a nonmonotone
network rather than monotone can be quite significant as demonstrated
on the example of computing symmetric functions and of summing two
natural numbers.
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