Application of back-propagation neural networks to the modeling and control of multiple-input, multiple-output processes

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dc.contributor.advisorSan, Ka-Yiuen_US
dc.creatorTakasu, Shinjien_US
dc.date.accessioned2009-06-04T00:00:02Zen_US
dc.date.available2009-06-04T00:00:02Zen_US
dc.date.issued1991en_US
dc.description.abstractCertain properties of back-propagation neural networks have been found to be useful in structuring models for multiple-input, multiple-output (MIMO) processes. The network's simplicity and its ability to identify the non-linearity can have wide impacts on the construction of model-based control system. Care must be taken to train the network with consistent data that contains sufficient dynamic information. A predictive control system based on the network model was proposed. Although the controller is relatively simple in terms of concept and computation, it shows excellent performances both in servo and regulator problems. Model prediction error sometimes causes a cyclic behavior in process responses; however, it can be stabilized by imposing certain constraints of controller action. The constraints are also effective for noisy measurements. Use of neural networks for modeling and control of MIMO system appears to be very promising with its ability to treat non-linearity and process interactions.en_US
dc.format.extent146 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoThesis Ch.E. 1991 Takasuen_US
dc.identifier.citationTakasu, Shinji. "Application of back-propagation neural networks to the modeling and control of multiple-input, multiple-output processes." (1991) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/13544">https://hdl.handle.net/1911/13544</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/13544en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectChemical engineeringen_US
dc.subjectArtificial intelligenceen_US
dc.titleApplication of back-propagation neural networks to the modeling and control of multiple-input, multiple-output processesen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentChemical Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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