Application of back-propagation neural networks to system identification and process control

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dc.contributor.advisorSan, Ka-Yiuen_US
dc.creatorBroussard, Mark Randallen_US
dc.date.accessioned2009-06-04T00:11:39Zen_US
dc.date.available2009-06-04T00:11:39Zen_US
dc.date.issued1991en_US
dc.description.abstractCertain properties of the back-propagation neural network have been found to be potentially useful in structuring models for process control applications. The network's relative simplicity and its ability to learn by example are potentially important in the effort to develop automated continuous on-line system identification. The capacity of the network to form nonlinear mappings enhances research designed to advance nonlinear system identification techniques. Since most real processes are nonlinear, this prospect can have wide impact. The unstructured nature of the network model was found to be controllable by techniques developed in the study. Care must be taken to identify and train the network with consistent data that contains sufficient dynamical information. Model-based fine tuning of a controller using a network model that was identified with closed-loop data was successful for the linear and nonlinear systems examined. The utility of the model is a function of the dynamical history of the process. When the information content of the data is sufficient, the network can capture the most important features of system behavior so that fine tuning can be based on optimal parameters such as integral absolute error. This method offers a more complete picture of tuning options than that of other fine tuning techniques such as trial and error, which are not based on a system model. The techniques developed in the tuning effort may be extended to closed-loop model identification for the purpose of controller redesign. In this case, successful identification probably depends on the continuous on-line identification to correct for modeling error.en_US
dc.format.extent260 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS CH.E. 1991 BROUSSARDen_US
dc.identifier.citationBroussard, Mark Randall. "Application of back-propagation neural networks to system identification and process control." (1991) Diss., Rice University. <a href="https://hdl.handle.net/1911/16420">https://hdl.handle.net/1911/16420</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/16420en_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.titleApplication of back-propagation neural networks to system identification and process controlen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentChemical Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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