Abstract
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 represents a Kalman-like filter and it 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 one. The obtained RNN model is simplified and used to design a Sliding Mode Control (SMC). The graphical simulation results of biopile system approximation, obtained via RNN model learning and the designed process SMC exhibited a good convergence, and precise system reference tracking.
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Baruch, I., Mariaca-Gaspar, CR., Cruz-Vega, I., Barrera-Cortes, J. (2007). Sliding Mode Control of a Hydrocarbon Degradation in Biopile System Using Recurrent Neural Network Model. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_113
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DOI: https://doi.org/10.1007/978-3-540-76631-5_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76630-8
Online ISBN: 978-3-540-76631-5
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