{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/43160"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/43160","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Investigation of different approaches for identification and control of complex and nonlinear systems using neural networks","abstract":"System 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.","abstract_html":"System 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.","abstract_has_math":false,"creators":["Tripathi, Nishith D."],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1994,"date_issued":"1994","date_published":"1994","updated_at":"2026-07-22T22:20:37Z","subjects":[],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-06112009-063450"],"render_values":[{"text":"etd-06112009-063450","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/43160","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Tripathi, Nishith D."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T21:37:57Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T21:37:57Z","2009-06-11"]},{"key":"dc:date.issued","label":"Date","values":["1994"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.dcmitype","label":"Dc Type Dcmitype","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-06112009-063450"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/43160"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["System 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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["BTD"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Investigation of different approaches for identification and control of complex and nonlinear systems using neural networks"]}]}],"canonical_facts":{"dc:contributor.department":["Electrical Engineering"],"dc:creator":["Tripathi, Nishith D."],"dc:date.accessioned":["2014-03-14T21:37:57Z"],"dc:date.available":["2014-03-14T21:37:57Z","2009-06-11"],"dc:date.issued":["1994"],"dc:description.abstract":["System 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."],"dc:description.degree":["Master of Science"],"dc:format.medium":["BTD"],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["etd-06112009-063450"],"dc:identifier.uri":["http://hdl.handle.net/10919/43160"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:title":["Investigation of different approaches for identification and control of complex and nonlinear systems using neural networks"],"dc:type":["Thesis"],"dc:type.dcmitype":["Text"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:37Z"}