In this paper, the modeling of complex systems using deep Elman neural network architecture is improved. The emphasis is to retrieve better deep Elman structure that emulates perfectly such dynamic systems. To achieve this goal, sigmoid activation functions in the hidden and output layers nodes are chosen and data files on considered systems for modeling and validation steps are given. Simulation results prove the ability and the efficiency of a deep Elman neural network with two hidden layers in this task.

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