ANN based Multi Classifier System for Prediction of High Energy Shower Primary Energy and Core Location
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.
[1] R. A. Mewaldt: "Cosmic Rays", Macmillan Encyclopedia of Physicsavailable
at http://www.srl.caltech.edu/personnel/dick/cos-encyc.html,
1996.
[2] R. Engel: "Theory and Phenomenology of Extensive Air
Showers", Forschungszentrum Karlsruhe, Germany- available at
http://moriond.in2p3.fr/J05/trans/sunday/engel1.pdf, 2005.
[3] D. Hanna, "Application of Neural Nets to Extensive Air-Showers",
Proceedings of the 22nd International Cosmic Ray Conference, IUPAP,
Volume 4, p: 500, 1991.
[4] J. C. Perrett and J T P M van Stekelenborg, "The applications of
neural networks in the core location analysis of extensive air showers",
Bartol Research Institute, University of Delaware, 217 Sharp Laboratory,
Newark, DE 19716, USA J. Phys. G: Nucl. Pert. Phys. 17, pp:1291-1302,
1991.
[5] H. J. Mayer, "A neural network algorithm for core location analysis at
large extended air shower arrays", Nuclear Instruments and Methods in
Physics Research, Section A: Accelerators, Spectrometers, Detectors and
Associated Equipment, Volume 317, Issues 1-2, pp. 339-345, June, 1992.
[6] P. Sommers: "Extensive Air Showers and Measurement Techniques",
Physics - Astrophysics, C. R. Acad. Sci. Paris, t. 4, Serie IV, pp. 1 - 12,
2004.
[7] T. Wibig, "The Artificial Neural Networks as a tool for analysis of
the individual Extensive Air Showers data", Preprint submitted to Elsevier
Preprint (1 February 2008), available at http://arxiv.org/abs/hepph/
9608227v1.
[8] A. Chilingarian, G. Gharagyozyan, S. Ghazaryan, G. Hovsepyan, E.
Mamidjanyan, L. Melkumyan, V. Romakhin, A. Vardanyan and S.
Sokhoyan: "Study of extensive air showers and primary energy spectra
by MAKET-ANI detector on mountain Aragats", Elsavier Journal of
Astroparticle Physics, vol. 28, pp. 5871, 2007.
[9] L. Chen and M. S. Kamel: "Design of Multiple Classifier Systems for
Time Series Data ", Spinger Lecture Notes in Computer Science, 2005,
vol. 3541, pp. 216-225, 2005.
[10] M. T. El-Melegy and S. M. Ahmed: "Neural Networks in Multiple
Classifier Systems for Remote-Sensing Image Classification ", Spinger
Studies in Fuzziness and Soft Computing, vol. 210, pp. 65-94, 2007.
[11] Z. Suraj, N. E. Gayar, P. Delimata: "A Rough Set Approach to
Multiple Classifier Systems", Fundamenta Informaticae, vol. 72, no. 1-3,
pp. 393-406, 2006.
[12] F. Roli: "Mini Tutorial on Multiple Classifier Systems", University of
Cagliari, Dept. of Electrical and Electronics Eng., Italy, School on the
Analysis of Patterns, 2009.
[13] M. Sharma and R. Mammone: "Speech recognition using sub-word
neural tree network models and multiple classifier fusion", CAIP Center,
Rutgers University, Piscataway, NJ, USA, 1995.
[14] M. Sharma and R. Mammone: "Artificial neural network fusion:
Application to Arabic words recognition", Proceedings of ESANN - 2005-
European Symposium on Artificial Neural Networks Bruges, (pp. 151 -
156), Belgium, 27-29 April, 2005.
[15] B. Yegnanarayana, Artificial Neural Networks, 1st Ed., PHI, New
Delhi, 2003.
[16] S. Haykin, Neural Networks A Comprehensive Foundation, 2nd Ed.,
Pearson Education, New Delhi, 2003.
[17] M. Hajmeer and I. Basheer: "A probabilistic neural network approach
for modeling and classification of bacterial growth/no-growth data",
Elsavier Journal of Microbiological Methods, vol. 51, pp. 217 226, 2002.
[18] "Probabilistic Neural Network Tutorial", available at
http://www.cse.unr.edu/ looney/cs773b/PNNtutorial.pdf.
[19] C. Kramer, B. Mckay, and J. Belina: "Probabilistic neural network array
architecture for ECG classification," Proc. Annu. Int. Conf. IEEE Eng.
Medicine Biol., vol. 17, pp. 807808, 1995.
[20] M. T. Musavi, K. H. Chan, D. M. Hummels, and K. Kalantri: "On the
generalization ability of neural-network classifier," IEEE Transactions on
Pattern Anal. Machine Intell., vol. 16, no. 6, pp. 659663, 1994.
[21] R. D. Romero, D. S. Touretzky, and G. H. Thibadeau: "Optical Chinese
character recognition using probabilistic neural networks," Pattern
Recognit., vol. 3, no. 8, pp. 12791292, 1997.
[22] D. F. Specht: "Probabilistic neural networks," Neural Networks, vol. 3,
no. 1, pp. 109118, 1990.
[23] Y. N. Sun, M. H. Horng, X. Z. Lin, and J. Y. Wang: "Ultrasonic image
analysis for liver diagnosis-a-noninvasive alternative to determine liver
disease," IEEE Eng. Med. Biol. Mag., vol. 15, no. 1, pp. 93101, 1996.
[24] P. Burrascano: "Learning vector quantization for the probabilistic neural
network," IEEE Transactions on Neural Networks, vol. 2, pp. 458461,
July 1991.
[25] P. P. Raghu and B. Yegnanarayana: "Supervised texture classification
using a probabilistic neural network and constraint satisfaction model,"
IEEE Transactions on Neural Networks, vol. 9, pp. 516522, May 1998.
[26] "Enhancements to the probabilistic neural networks," Proc. of IEEE Int.
Joint Conf. Neural Networks, Baltimore, MD, pp. 761768, 1992.
[27] H. G. C. Traven: "A neural-network approach to statistical pattern classification
by semiparametric estimation of a probability density functions,"
IEEE Transactions on Neural Networks, vol. 2, pp. 366377, 1991.
[28] A. Zaknich: "A vector quantization reduction method for the probabilistic
neural network," Proc. IEEE Int. Conf. Neural Networks, Piscataway,
NJ, pp. 11171120, 1997.
[29] K. Z. Mao, K. C. Tan, and W. Ser: "Probabilistic Neural-Network
Structure Determination for Pattern Classification," IEEE Transactions on
Neural Networks, vol. 11, no. 4, July, 2000.
[30] R. Rojas, Neural Networks-A Systematic Introduction, Springer, Berlin,
1996.
[1] R. A. Mewaldt: "Cosmic Rays", Macmillan Encyclopedia of Physicsavailable
at http://www.srl.caltech.edu/personnel/dick/cos-encyc.html,
1996.
[2] R. Engel: "Theory and Phenomenology of Extensive Air
Showers", Forschungszentrum Karlsruhe, Germany- available at
http://moriond.in2p3.fr/J05/trans/sunday/engel1.pdf, 2005.
[3] D. Hanna, "Application of Neural Nets to Extensive Air-Showers",
Proceedings of the 22nd International Cosmic Ray Conference, IUPAP,
Volume 4, p: 500, 1991.
[4] J. C. Perrett and J T P M van Stekelenborg, "The applications of
neural networks in the core location analysis of extensive air showers",
Bartol Research Institute, University of Delaware, 217 Sharp Laboratory,
Newark, DE 19716, USA J. Phys. G: Nucl. Pert. Phys. 17, pp:1291-1302,
1991.
[5] H. J. Mayer, "A neural network algorithm for core location analysis at
large extended air shower arrays", Nuclear Instruments and Methods in
Physics Research, Section A: Accelerators, Spectrometers, Detectors and
Associated Equipment, Volume 317, Issues 1-2, pp. 339-345, June, 1992.
[6] P. Sommers: "Extensive Air Showers and Measurement Techniques",
Physics - Astrophysics, C. R. Acad. Sci. Paris, t. 4, Serie IV, pp. 1 - 12,
2004.
[7] T. Wibig, "The Artificial Neural Networks as a tool for analysis of
the individual Extensive Air Showers data", Preprint submitted to Elsevier
Preprint (1 February 2008), available at http://arxiv.org/abs/hepph/
9608227v1.
[8] A. Chilingarian, G. Gharagyozyan, S. Ghazaryan, G. Hovsepyan, E.
Mamidjanyan, L. Melkumyan, V. Romakhin, A. Vardanyan and S.
Sokhoyan: "Study of extensive air showers and primary energy spectra
by MAKET-ANI detector on mountain Aragats", Elsavier Journal of
Astroparticle Physics, vol. 28, pp. 5871, 2007.
[9] L. Chen and M. S. Kamel: "Design of Multiple Classifier Systems for
Time Series Data ", Spinger Lecture Notes in Computer Science, 2005,
vol. 3541, pp. 216-225, 2005.
[10] M. T. El-Melegy and S. M. Ahmed: "Neural Networks in Multiple
Classifier Systems for Remote-Sensing Image Classification ", Spinger
Studies in Fuzziness and Soft Computing, vol. 210, pp. 65-94, 2007.
[11] Z. Suraj, N. E. Gayar, P. Delimata: "A Rough Set Approach to
Multiple Classifier Systems", Fundamenta Informaticae, vol. 72, no. 1-3,
pp. 393-406, 2006.
[12] F. Roli: "Mini Tutorial on Multiple Classifier Systems", University of
Cagliari, Dept. of Electrical and Electronics Eng., Italy, School on the
Analysis of Patterns, 2009.
[13] M. Sharma and R. Mammone: "Speech recognition using sub-word
neural tree network models and multiple classifier fusion", CAIP Center,
Rutgers University, Piscataway, NJ, USA, 1995.
[14] M. Sharma and R. Mammone: "Artificial neural network fusion:
Application to Arabic words recognition", Proceedings of ESANN - 2005-
European Symposium on Artificial Neural Networks Bruges, (pp. 151 -
156), Belgium, 27-29 April, 2005.
[15] B. Yegnanarayana, Artificial Neural Networks, 1st Ed., PHI, New
Delhi, 2003.
[16] S. Haykin, Neural Networks A Comprehensive Foundation, 2nd Ed.,
Pearson Education, New Delhi, 2003.
[17] M. Hajmeer and I. Basheer: "A probabilistic neural network approach
for modeling and classification of bacterial growth/no-growth data",
Elsavier Journal of Microbiological Methods, vol. 51, pp. 217 226, 2002.
[18] "Probabilistic Neural Network Tutorial", available at
http://www.cse.unr.edu/ looney/cs773b/PNNtutorial.pdf.
[19] C. Kramer, B. Mckay, and J. Belina: "Probabilistic neural network array
architecture for ECG classification," Proc. Annu. Int. Conf. IEEE Eng.
Medicine Biol., vol. 17, pp. 807808, 1995.
[20] M. T. Musavi, K. H. Chan, D. M. Hummels, and K. Kalantri: "On the
generalization ability of neural-network classifier," IEEE Transactions on
Pattern Anal. Machine Intell., vol. 16, no. 6, pp. 659663, 1994.
[21] R. D. Romero, D. S. Touretzky, and G. H. Thibadeau: "Optical Chinese
character recognition using probabilistic neural networks," Pattern
Recognit., vol. 3, no. 8, pp. 12791292, 1997.
[22] D. F. Specht: "Probabilistic neural networks," Neural Networks, vol. 3,
no. 1, pp. 109118, 1990.
[23] Y. N. Sun, M. H. Horng, X. Z. Lin, and J. Y. Wang: "Ultrasonic image
analysis for liver diagnosis-a-noninvasive alternative to determine liver
disease," IEEE Eng. Med. Biol. Mag., vol. 15, no. 1, pp. 93101, 1996.
[24] P. Burrascano: "Learning vector quantization for the probabilistic neural
network," IEEE Transactions on Neural Networks, vol. 2, pp. 458461,
July 1991.
[25] P. P. Raghu and B. Yegnanarayana: "Supervised texture classification
using a probabilistic neural network and constraint satisfaction model,"
IEEE Transactions on Neural Networks, vol. 9, pp. 516522, May 1998.
[26] "Enhancements to the probabilistic neural networks," Proc. of IEEE Int.
Joint Conf. Neural Networks, Baltimore, MD, pp. 761768, 1992.
[27] H. G. C. Traven: "A neural-network approach to statistical pattern classification
by semiparametric estimation of a probability density functions,"
IEEE Transactions on Neural Networks, vol. 2, pp. 366377, 1991.
[28] A. Zaknich: "A vector quantization reduction method for the probabilistic
neural network," Proc. IEEE Int. Conf. Neural Networks, Piscataway,
NJ, pp. 11171120, 1997.
[29] K. Z. Mao, K. C. Tan, and W. Ser: "Probabilistic Neural-Network
Structure Determination for Pattern Classification," IEEE Transactions on
Neural Networks, vol. 11, no. 4, July, 2000.
[30] R. Rojas, Neural Networks-A Systematic Introduction, Springer, Berlin,
1996.
@article{"International Journal of Electrical, Electronic and Communication Sciences:55532", author = "Gitanjali Devi and Kandarpa Kumar Sarma and Pranayee Datta and Anjana Kakoti Mahanta", title = "ANN based Multi Classifier System for Prediction of High Energy Shower Primary Energy and Core Location", abstract = "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.", keywords = "EAS, Shower, Core, ANN, Location.", volume = "4", number = "3", pages = "522-10", }
