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dc.contributor.authorKim, Jaesooen_NZ
dc.contributor.authorKozma, Roberten_NZ
dc.contributor.authorKasabov, Nikolaen_NZ
dc.contributor.authorGols, Ben_NZ
dc.contributor.authorGeerink, Men_NZ
dc.contributor.authorCohen, Ten_NZ
dc.date.available2011-04-07T03:06:47Z
dc.date.copyright1998-03en_NZ
dc.identifier.citationKim, 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/10523/1146en
dc.identifier.urihttp://hdl.handle.net/10523/1146
dc.descriptionPlease 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.abstractBiological 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.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleA fuzzy neural network model for the estimation of the feeding rate to an anaerobic waste water treatment processen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages13en_NZ
otago.date.accession2011-01-13 19:57:48en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints1031en_NZ
otago.school.eprintsKnowledge Engineering Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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otago.relation.number98/05en_NZ
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