13-04-2013, 03:26 PM
ARTIFICIAL NEURAL NETWORKS: A TUTORIAL
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INTRODUCTION
Artificial Neural Network (ANN) or Neural Network(NN)
has provide an exciting alternative method for solving a
variety of problems in different fields of science and
engineering.
This article is trying to give the readers a :
- Whole idea about ANN
- Motivation for ANN development
- Network architecture and learning models
- Outline some of the important use of ANN
Origin of Neural Network
Human brain has many incredible characteristics such as massive
parallelism, distributed representation and computation, learning
ability, generalization ability, adaptivity, which seems simple but is
really complicated.
It has been always a dream for computer scientist to create a
computer which could solve complex perceptual problems this fast.
ANN models was an effort to apply the same method as human
brain uses to solve perceptual problems.
Three periods of development for ANN:
- 1940:Mcculloch and Pitts: Initial works
- 1960: Rosenblatt: perceptron convergence theorem
Minsky and Papert: work showing the limitations of a simple
perceptron
- 1980: Hopfield/Werbos and Rumelhart: Hopfield's energy
approach/back-propagation learning algorithm
Biological Neural Network
When a signal reaches a synapse: Certain
chemicals called neurotransmitters are
released.
Process of learning: The synapse
effectiveness can be adjusted by signal
passing through.
Cerebral cortex :a large flat sheet of
neurons about 2 to 3 mm thick and 2200
cm , 10^11 neurons
Duration of impulses between neurons:
milliseconds and the amount of
information sent is also small(few bits)
Critical information are not transmitted
directly , but stored in interconnections
The term Connectionist model initiated
from this idea.
Hebbian Rules
One of the oldest learning rule initiated
form neurobiological experiments
The basic concept of Hebbian Learning:
when neuron A activates, and then causes
neuron B to activate, then the connection
strength between the two neurons is
increased, and it will be easier for A to
activate B in the future.
Learning is done locally, it means weight of
a connection is corrected only with respect
to neurons connected to it.
Orientation selectivity: occurs due to
Hebbian training of a network
Competitive Learning Rules
The basis is the “winner take all” originated from biological
Neural network
All input units are connected together and all units of output are
also connected via inhibitory weights but gets feed back with
excitory weight
Only one of the unites with largest or smallest input is activated
and its weight becomes adjusted
As a result of learning process the pattern in the winner unit
(weight) become closer to he input pattern
Summery
A great overview of ANN is presented in this paper, it is very
easy understanding and straightforward
The different types of learning rules, algorithms and also
different architectures are well explained
A number of Networks were described through simple words
The popular applications of NN were illustrated
The author Believes that ANNS brought up both enthusiasm
and criticism.
Actually except for some special problems there is no evidence
that NN is better working than other alternatives
More development and better performance in NN requires
the combination of ANN with new technologies