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Neural networks are a model of intelligence models and are known as (Nodes, Neurons) to watch them with the property of experience, experimental information, to make them, by adjusting the weights. It is analogous to the human brain in an industry that gains knowledge by training and stores knowledge using forces within neurons called synaptic weights. There is also a dynamic ring, which gives biologists the opportunity to draw on these networks to understand the evolution of biological phenomena.
This thesis deals with a theoretical study of some coefficients of solid physics and some special transactions for hadron collisions at high energies within the framework of the neural networks model to simulate these parameters as well as predict values that have not been practically performed before with a description of them with a mathematical equation obtained from this model.