Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/80739
Title: Modeling pH neutralization processes using fuzzy-neural approaches
Authors: Nie, J. 
Loh, A.P. 
Hang, C.C. 
Keywords: Fuzzy modeling
Fuzzy-neural systems
Neural networks
pH nonlinear process
Process control
Issue Date: 1996
Citation: Nie, J.,Loh, A.P.,Hang, C.C. (1996). Modeling pH neutralization processes using fuzzy-neural approaches. Fuzzy Sets and Systems 78 (1) : 5-22. ScholarBank@NUS Repository.
Abstract: This paper is concerned with the modeling and identification of pH-processes via fuzzy-neural approaches. A simplified fuzzy model acting as an approximate reasoner is used to deduce the model output on the basis of the identified rule-base which is derived by using one of the following three network-based self-organizing algorithms: unsupervised self-organizing counter-propagation network (USOCPN), supervised self-organizing counter-propagation network (SSOCPN), and self-growing adaptive vector quantization (SGAVQ). Three typical pH processes were treated including a strong acid-strong base system, a weak acid-strong base system, and a two-output system with buffering taking part in reaction. Extensive simulations including on-line modeling have shown that these nonlinear pH-processes can be modeled reasonably well by the present schemes which are simple but efficient. © 1996 - Elsevier Science B.V. All rights reserved.
Source Title: Fuzzy Sets and Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/80739
ISSN: 01650114
Appears in Collections:Staff Publications

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