Investigation of different approaches for identification and control of complex and nonlinear systems using neural networks

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dc.contributor.authorTripathi, Nishith D.en
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2014-03-14T21:37:57Zen
dc.date.adate2009-06-11en
dc.date.available2014-03-14T21:37:57Zen
dc.date.issued1994en
dc.date.rdate2009-06-11en
dc.date.sdate2009-06-11en
dc.description.abstractSystem identification deals with the problem of building mathematical models of dynamical systems based on observed data from the systems. Most of the conventional techniques of system identification, in general, require some amount of a priori knowledge about the structure of the systems. Also, they are only useful either with linear or linearized systems. There are numerous control principles working nicely in industry. However, they are less effective for MIMO systems or complex nonlinear systems. The need to control, in a better way, increasingly complex dynamical systems under significant uncertainty has made the need for new methods quite apparent. This thesis investigates different approaches for identification and control of complex nonlinear systems using neural networks. For system identification and control, ANN properties of generalization and their capability of extracting complex relationships among inputs presented to them are useful. Two different techniques, called whole region method (WRM) and the separate regions method (SRM) technique, have been developed and applied to two classes of nonlinear systems. Different connectionist control techniques such as adaptive control and neuro-PID control have been developed and applied to the robotic manipulators.en
dc.description.degreeMaster of Scienceen
dc.format.extentix, 114 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-06112009-063450en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-06112009-063450/en
dc.identifier.urihttp://hdl.handle.net/10919/43160en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V855_1994.T757.pdfen
dc.relation.isformatofOCLC# 32457896en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V855 1994.T757en
dc.subject.lcshNeural networks (Computer science)en
dc.subject.lcshSystem identificationen
dc.titleInvestigation of different approaches for identification and control of complex and nonlinear systems using neural networksen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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