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Autonomic Computing for a Complex Problem of Experimental Physics
Abstract
Standard experimental data analysis is based mainly on conventional, deterministic inference. The complexity of modern physics problems has become so large that new ideas in the field are received with the highest of appreciation. In this paper, the author has analyzed the problem of contemporary high-energy physics concerning the estimation of some parameters of the observed complex phenomenon. This article confronts the Natural and Artificial Networks performance with the standard statistical method of the data analysis and minimization. The general concept of the relations between CI and standard (external) classical and modern informatics was realized and studied by utilizing of Natural Neural Networks (NNN), Artificial Neural Networks (ANN) and MINUIT minimization package from CERN. The idea of Autonomic Computing was followed by using brains of high school students involved in the Roland Maze Project. Some preliminary results of the comparison are given and discussed.
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