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DETERMINISTIC NEURON -A MODEL FOR FASTER LEARNING Farid Ahmed and Abdul Ahad S. Awwal
 

Summary: DETERMINISTIC NEURON - A MODEL FOR FASTER LEARNING
Farid Ahmed and Abdul Ahad S. Awwal
Wright State University
Computer Science & Engineering Department
Dayton, OH 45435.
Abstract
Training in most neural network architectures are
currently being done by updating the weights of
the network in a way to reduce some error mea-
sures. The well-known backpropagation algorithm
and some other training algorithms use this ap-
proach. Obviously, this has been very successful in
mimicking the way the biological neurons do their
function. But the problem of slow learning and get-
ting trapped in local minimas of error function do-
main deserve serious investigation. Various models
are proposed with various levels of success to get rid
of these two problems. In this work, we propose a
deterministic model of the neuron, that guarantees
faster learning by modifying the nonlinearity asso-

  

Source: Ahmed, Farid - Department of Electrical Engineering and Computer Science, Catholic University of America

 

Collections: Engineering