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Backpropagation
COUNTERPROPAGATION NETWORKS
INTRODUCTION
Counter propagation is a combination of two well-known algorithms: the selforganizing map of Kohonen (1988) and the Grossberg(1969, 1971, 1982) outstar.
Methods such as counterpropagation that combine network paradigms in building-block
fashion may produce networks closer to the brain's architecture than any homogeneous
structure.
As in many other networks, counterpropagation functions in two modes: the
normal mode, in which it accepts an input vector X and produces an output vector Y, and
the training mode, in which an input vector is applied and the weights are adjusted to
yield the desired output vector.
TRAINING ALGORITHM
1. Apply a set of inputs and compute the resulting outputs.
2. Compare these outputs with the desired outputs and calculate a measure of their
difference. A commonly used method finds the difference between the actual and desired
outputs of each element of a trainin pair, squares the differences, and sums all of the
squares. The object of training is to minimize this difference, often called the objective
function.
3. Select a weight at random and adjust it by a small random amout. If the
adjustment helps (reduces the objective function), retain it; otherwise, return the weight to
its previous value.
4. Repeat steps 1 through 3 until the network is trained to the desired degree.
APPLICATIONS
The discussion up to this point has assumed that we are adjusting weights in
atraditional artificial neural network. However this is a special case;these statistical
techniques are far more general and are capable of solving a variety of problems in
nonlinear optimization.
A NONLINEAR OPTIMIZATION PROBLEM involves a set of independent
varibles that are related to the value of an objective function in a deterministic fashion.
the goal of this problem is to find the set of values for the indepedent variables that
minimizes (or maximizes) the objectivefunction.
ARTIFICIAL NEURAL NETWORKS TODAY
Automated and robotic factories are now being monitored by ANNs that contol
machinery, adjust temperature settings, diagnose malfunctions and more. These ANNs
can augment or replace skilled labour, making it possible for fewer people to do more
work.
Large financial institutions have used ANNs to imporve performance in such
areas as bond rating, credit scoring, target marketing and evaluating loan application,
ANNs are now used to analyze credit card transactions to detect likely instaces of fraud.
Bonus detectors in many US airports use ANNs to analyze airborne frace elements
to sense the presence of explosive chemicals. Chicago police department uses ANNs to
try to root out corruption among police officers.
PROSPECTS FOR THE FUTURE
Artificial neural networks have been proposed for tasks ranging from battlefield
management and minding the baby. Dull, repetitive, or dangerous tasks can be performed
by machines and entirely new appllications will arise as the technology matures.
Today's surge of interest has set thousands of researchers to work in the field. It is
reasonable to expect a rapid increase in our understanding of ANNs leading to improved
network paradigms and a host of application opportunities.
REFERENCES
Neural Computing
by Philip D.Wasserman.
Neural Networks and Computer Intelligence
by LiMin Fu.