Neural Network Modelling and Sliding Mode Control of a Hydrocarbon Degradation Process

I.S. Baruch, C.R. Mariaca-Gaspar, I.Cruz, and J. Barrera-Cortes (Mexico)


Recurrent neural networks model, backpropagation learning algorithm, Kalman filter, sliding mode control, hydrocarbon degradation, and biopile system.


This paper proposes the use of a Recurrent Neural Network (RNN) for modeling a hydrocarbon degradation process carried out in a biopile system. The proposed RNN model has seven inputs, five outputs and twelve neurons in the hidden layer, with global and local feedbacks. The learning algorithm is a modified version of the dynamic backpropagation. The obtained RNN model is used to design a sliding mode control. The simulation results obtained with the RNN model learning and control exhibit a good convergence and tracking.

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