Details

Title

Neuro-fuzzy control design of processes in chemical technologies

Journal title

Archives of Control Sciences

Yearbook

2012

Numer

No 2

Publication authors

Divisions of PAS

Nauki Techniczne

Description

Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.

Aims and Scope: Archives of Control Sciences publishes papers in the broadly understood field of control science and related areas while promoting the closer integration of the Polish, as well as other Central and East European scientific communities with the international world of science.

Publisher

Committee of Automatic Control and Robotics PAS

Date

2012

Identifier

ISSN 1230-2384

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.

DOI

10.2478/v10170-011-0022-2

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