Abstract
The development of computational models capable of accurately describe a process’s dynamic response is a task ultimately dependent on its physical phenomena complexity and the type of disturbances that may affect its operation.
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References
Iserman, M., Münchhof, M.: Identification of Dynamic Systems. Springer, Heidelberg (2011)
Nørgaard, M., Ravn, O., Poulsen, K., Hansen, L.: Neural Networks for Modeling and Control of Dynamic Systems. Springer, London (2000)
Ljung, L.: System Identification: Theory for the User. Prentice Hall (1987)
Åström, K.; Wittenmark, B.: Adaptive Control. Dover, London (2008)
Clarke, D., Mohtadi, C., Tuffs, P.: Generalized predictive control, parts 1 and 2. Autom. 23, 137–160 (1987)
Herceg, M., Kvasnica, M., Fikar, M.: Transformation of Fuzzy Takagi-Sugeno Models into Piecewise Affine Models. Rough Sets and Intelligent Systems Paradigms, Lecture Notes in Computer Science 4585, 211–220 (2007)
Mendes, J.: Computational Intelligence Methodologies for Control of Industrial Processes, Ph.D. Thesis, Department of Electrical and Computer Engineering, University of Coimbra (2014)
Bishop, C.: Pattern recognition and Machine Learning, Information Sciences and Statistics, 1st edn. Springer, New York (2006)
Ławryńczuk, M.: Computationally Efficient Model Predictive Control Algorithms – A Neural Network Approach, Studies in Systems, Decision and Control, vol. 3. Springer (2014)
Ogata, K.: Modern Control Engineering, 5th edn. Prentice Hall (2009)
Nagy, Z.: Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks. Chemical Engineering Journal, 127: 95–109. Elsevier(2007)
Luyben, W.: Chemical Reactor Design and Control. Wiley, New York (2007)
Wu, D., Tan, W.: A simplified Type-2 Fuzzy logic controller for real-time control. ISA Trans. 45(4), 503–516 (2006)
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Antão, R., Mota, A., Escadas Martins, R., Tenreiro Machado, J. (2017). System Modeling Using Type-2 Takagi-Sugeno Fuzzy Systems. In: Type-2 Fuzzy Logic. Nonlinear Physical Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-4633-9_4
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DOI: https://doi.org/10.1007/978-981-10-4633-9_4
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