Optimization of system parameters controlling electric furnace temperature using genetic algorithms
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The optimization of the parameters of the electric furnace temperature control was considered. The optimization was executed using genetic algorithms. The model takes into account nonlinearity, which is connected with the penetration of heat. Also, it is connected with losses of heat due to convection and radiation. The genetic algorithm determines the selection of parameters of the mathematical model in which the system accurately reproduces the input action.
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