Fuzzy and neuro-fuzzy modeling of a fermentation process
Joint Authors
al-Shabbi, Sharif
al-Taibi, Mahmud
Vincent, Nicole
Source
The International Arab Journal of Information Technology
Issue
Vol. 6, Issue 4 (31 Oct. 2009), pp.378-384, 7 p.
Publisher
Publication Date
2009-10-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Neuro-fuzzy modeling may be qualified as a grey-box technique, since it combines the transparency of rule-based fuzzy system with the learning capability of neural networks.
The main problem in the identification of non-linear processes is the lack of complete information.
Certain variables are, either immeasurable or difficult to measure, the soft sensors are the necessary tools to solve the problem.
Those latter can be used via online estimation, and then will be implemented in fed- batch fermentation processes for optimal production and online monitoring.
The process parameters are estimented through a fuzzy logic system.
the fuzzy models of takagi-sugeno type suffer of the problem of poor initialization, which can be solved by the trial-and error method trial-and- error method is used to solve the poor initialization problem of TS models, this deals with identifying the structure of the model, such structure consists on finding the optimum number of rules, which enters in the model cost reduction.
The fuzzy model might not capture the process non-linearity, especially if the number of rules is over-optimized bioreactors exhibit a wide range of dynamic behaviours and offer many challenges to modeling, as a result of the presence of living micro-organisms whose growth rate is described by complex equations.
we will illustrate the fuzzy and the neuro-fuzzy modeling on the identification of such a system.
in order to compare the NF model outputs, we use another fuzzy model that does not incorporate the neural network learning capability, to identify the parameters of the same process.
even thought, the two models were trained using levenberg-marquardt algorithm, the corresponding simulation result show that a better modeling is achieved using NF technique, especially that we did not employ any involved optimization procedure to identify the NF structure.
American Psychological Association (APA)
al-Shabbi, Sharif& al-Taibi, Mahmud& Vincent, Nicole. 2009. Fuzzy and neuro-fuzzy modeling of a fermentation process. The International Arab Journal of Information Technology،Vol. 6, no. 4, pp.378-384.
https://search.emarefa.net/detail/BIM-10235
Modern Language Association (MLA)
al-Shabbi, Sharif…[et al.]. Fuzzy and neuro-fuzzy modeling of a fermentation process. The International Arab Journal of Information Technology Vol. 6, no. 4 (Oct. 2009), pp.378-384.
https://search.emarefa.net/detail/BIM-10235
American Medical Association (AMA)
al-Shabbi, Sharif& al-Taibi, Mahmud& Vincent, Nicole. Fuzzy and neuro-fuzzy modeling of a fermentation process. The International Arab Journal of Information Technology. 2009. Vol. 6, no. 4, pp.378-384.
https://search.emarefa.net/detail/BIM-10235
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 378-384
Record ID
BIM-10235