Neural Network Performance for Complex Minimization Problem
Tadeusz Wibig
DOI: 10.4236/cn.2010.21004   PDF   HTML     3,717 Downloads   7,089 Views  


We have analyzed the important problem of contemporary high-energy physics concerning the estimation of some parameters of the observed complex phenomenon. The standard statistical method of the data analysis and minimization was confronted with the Neural Network approaches. For the Natural Neural Networks we have used brains of high school students involved in our Roland Maze Project. The excitement of active participation in real scientific work produced their astonishing performance what is described in the present work. Some preliminary results are given and discussed.

Share and Cite:

T. Wibig, "Neural Network Performance for Complex Minimization Problem," Communications and Network, Vol. 2 No. 1, 2010, pp. 31-37. doi: 10.4236/cn.2010.21004.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] D. Barnhill, et al., [Pierre Auger Collaboration], Measurement of the lateral distribution function of UHECR air showers with the Pierre Auger observatory, Proceedings of the 29th International Cosmic Ray Conference, Pune, India, pp. 101–104; arXiv:astro-ph/0507590, 2005.
[2] J. Feder, et al., “The roland maze project: school-based extensive air shower network,” Nuclear Physics Proceedings Supplements, No. 151, pp. 430–433, 2006.
[3] F. James and M. Roos, “Minuit: A system for function minimization and analysis of the parameter errors and correlations,” Computer Physics Communications, Vol. 10, pp. 343–367, 1975.
[4] H. O. Klages, et al., “The KASCADE experiment,” Nuclear Physics Proceedings Supplements, No. 52B, pp. 92– 102, 1997.
[5] T. Wibig, The artificial neural networks in cosmic ray physics experiment; I. Total muon number estimation. In A. P. del Pobil and J. Mira (Eds.) Lecture notes in computer science; Vol. 1416: Lecture Notes in Artificial Intelligence; Vol. 2 Tasks and methods in applied artificial intelligence, Springer-Verlag, Berlin, Heidelberg, New York, pp. 867, 1998.

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.