A fuzzy neural network model for the estimation of the feeding rate to an anaerobic waste water treatment process

dc.contributor.author	Kim, Jaesoo	en_NZ
dc.contributor.author	Kozma, Robert	en_NZ
dc.contributor.author	Kasabov, Nikola	en_NZ
dc.contributor.author	Gols, B	en_NZ
dc.contributor.author	Geerink, M	en_NZ
dc.contributor.author	Cohen, T	en_NZ
dc.date.copyright	1998-03	en_NZ
dc.identifier.citation	Kim, J., Kozma, R., Kasabov, N., Gols, B., Geerink, M., & Cohen, T. (1998). A fuzzy neural network model for the estimation of the feeding rate to an anaerobic waste water treatment process (Information Science Discussion Papers Series No. 98/05). University of Otago. Retrieved from http://hdl.handle.net/10523/1146	en
dc.identifier.uri	http://hdl.handle.net/10523/1146
dc.description	Please note that this is a searchable PDF derived via optical character recognition (OCR) from the original source document. As the OCR process is never 100% perfect, there may be some discrepancies between the document image and the underlying text.	en_NZ
dc.description.abstract	Biological processes are among the most challenging to predict and control. It has been recognised that the development of an intelligent system for the recognition, prediction and control of process states in a complex, nonlinear biological process control is difficult. Such unpredictable system behaviour requires an advanced, intelligent control system which learns from observations of the process dynamics and takes appropriate control action to avoid collapse of the biological culture. In the present study, a hybrid system called fuzzy neural network is considered, where the role of the fuzzy neural network is to estimate the correct feed demand as a function of the process responses. The feed material is an organic and/or inorganic mixture of chemical compounds for the bacteria to grow on. Small amounts of the feed sources must be added and the response of the bacteria must be measured. This is no easy task because the process sensors used are non-specific and their response would vary during the developmental stages of the process. This hybrid control strategy retains the advantages of both neural networks and fuzzy control. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the form of semantically meaningful fuzzy rules, and the ability to accommodate both numerical data and existing expert knowledge about the problem under consideration. The application to the estimation and prediction of the correct feed demand shows the power of this strategy as compared with conventional fuzzy control.	en_NZ
dc.format.mimetype	application/pdf
dc.publisher	University of Otago	en_NZ
dc.relation.ispartofseries	Information Science Discussion Papers Series	en_NZ
dc.subject.lcsh	QA76 Computer software	en_NZ
dc.title	A fuzzy neural network model for the estimation of the feeding rate to an anaerobic waste water treatment process	en_NZ
dc.type	Discussion Paper	en_NZ
dc.description.version	Unpublished	en_NZ
otago.bitstream.pages	13	en_NZ
otago.date.accession	2011-01-13 19:57:48	en_NZ
otago.school	Information Science	en_NZ
otago.openaccess	Open
otago.place.publication	Dunedin, New Zealand	en_NZ
dc.identifier.eprints	1031	en_NZ
otago.school.eprints	Knowledge Engineering Laboratory	en_NZ
otago.school.eprints	Information Science	en_NZ
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otago.relation.number	98/05	en_NZ