Back
 CN  Vol.2 No.1 , February 2010
Neural Network Performance for Complex Minimization Problem
Abstract: 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.
Cite this paper: nullT. 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.
References

[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.

 
 
Top