Details

Title

Neuro-fuzzy control design of processes in chemical technologies

Journal title

Archives of Control Sciences

Yearbook

2012

Issue

No 2

Authors

Divisions of PAS

Nauki Techniczne

Publisher

Committee of Automatic Control and Robotics PAS

Date

2012

Identifier

DOI: 10.2478/v10170-011-0022-2 ; ISSN 1230-2384

Source

Archives of Control Sciences; 2012; No 2

References

Armfield. Instruction manual PCT40, 4th edition, 2005. ; Armfield. Instruction manual PCT41, 3rd edition, 2006. ; Armfield. Instruction manual PCT42, 2nd edition, 2006. ; Åstroöm K. (1989), Adaptive Control. ; Babuška R. (2003), Neuro-fuzzy methods for nonlinear system identification, Annual Reviews in Control, 73, doi.org/10.1016/S1367-5788(03)00009-9 ; Ová M. (2009), Robust stabilization of a chemical reactor, Chemical Papers, 5, 63, 527. ; Bastin G. (1990), On-line estimation and adaptive control of bioreactors. ; Blahová L. (2010), In Latest Trends on Systems, 14, 336. ; Chu J. (2003), An experimental study of model predictive control based on artificial neural networks, null, 1296. ; J. Dennis, JR. (1983), Numerical Methods for Unconstrained Optimization and Nonlinear Equations. ; Dostal P. (2007), Adaptive control of a continuous stirred tank reactor by two feedback controllers, null. ; Henson M. (1997), Nonlinear process control. ; Jang J. (1993), Adaptive-network-based fuzzy inference system, IEEE Trans. on Systems, Man, and Cybernetics, 23, 665, doi.org/10.1109/21.256541 ; Kvasnica M. (2010), Model predictive control of a CSTR: A hybrid modeling approach, Chemical papers, 3, 64, 301, doi.org/10.2478/s11696-010-0008-8 ; Liu S. (2002), Robust control based on neuro-fuzzy systems for a continuous stirred tank reactor, null. ; Maciejowski J. (2001), Predictive Control with Constraints. ; Marquardt D. (1963), An algorithm for least squares estimation of nonlinear parameters, J. of Society for Industrial and Applied Mathematics, 11, 431, doi.org/10.1137/0111030 ; Mészáros A. (2009), Intelligent control of a pH process, Chemical Papers, 2, 63, 180, doi.org/10.2478/s11696-009-0005-y ; Mikleš J. (2007), Process Modeling, Identification, and Control. ; Morari M. (1989), Robust Process Control. ; Sámek D. (2008), Semi-batch reactor predictive control using artificial neural network, null, 1532. ; Takagi T. (1985), Fuzzy identification of fuzzy systems and its applications to modeling and control, IEEE Trans. Systems, Man and Cybernetics, 15, 116, doi.org/10.1109/TSMC.1985.6313399 ; The Mathworks: Neural Network Toolbox, User's Guide, 2002.

Open Access Policy

Archives of Control Sciences is an open access journal with all content available with no charge in full text version.


The journal content is available under the licencse CC BY-NC-ND 4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/.
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