%0 Journal Article
	%A Gitanjali Devi and  Kandarpa Kumar Sarma and  Pranayee Datta and  Anjana Kakoti Mahanta
	%D 2010
	%J International Journal of Electrical and Computer Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 39, 2010
	%T ANN based Multi Classifier System for Prediction of High Energy Shower Primary Energy and Core Location
	%U https://publications.waset.org/pdf/6450
	%V 39
	%X Cosmic showers, during the transit through space, produce
sub - products as a result of interactions with the intergalactic
or interstellar medium which after entering earth generate secondary
particles called Extensive Air Shower (EAS). Detection and analysis
of High Energy Particle Showers involve a plethora of theoretical and
experimental works with a host of constraints resulting in inaccuracies
in measurements. Therefore, there exist a necessity to develop a
readily available system based on soft-computational approaches
which can be used for EAS analysis. This is due to the fact that soft
computational tools such as Artificial Neural Network (ANN)s can be
trained as classifiers to adapt and learn the surrounding variations. But
single classifiers fail to reach optimality of decision making in many
situations for which Multiple Classifier System (MCS) are preferred
to enhance the ability of the system to make decisions adjusting
to finer variations. This work describes the formation of an MCS
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN) with data inputs
from correlation mapping Self Organizing Map (SOM) blocks and
the output optimized by another SOM. The results show that the setup
can be adopted for real time practical applications for prediction
of primary energy and location of EAS from density values captured
using detectors in a circular grid.
	%P 644 - 653