Abstract
The control of an unknown multivariable nonlinear process represents a challenging problem. Model based approaches, like Generalized Minimum Variance, provide a flexible framework for addressing the main issues arising in the control of complex nonlinear systems. However, the final performance will depend heavily on the models representing the system. This work presents a comparative analysis of two modelling approaches for nonlinear systems, namely Artificial Neural Network (ANN) and Gaussian processes. Their advantages and disadvantages as building blocks of a GMV controller are illustrated by simulation.
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Sbarbaro, D., Murray-Smith, R., Valdes, A. (2004). Multivariable Generalized Minimum Variance Control Based on Artificial Neural Networks and Gaussian Process Models. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_8
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DOI: https://doi.org/10.1007/978-3-540-28648-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
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