{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/69400"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/69400","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Improvements in Adaptive IIR Filtering: Theory and Application","abstract":"Characteristics of the mean-square error surface in adaptive digital filters determine how well a gradient algorithm performs within a given filter structure, i.e., if the surface has steep slopes and contains local minima, a gradient algorithm will have difficulty reaching the global minimum. It is shown that although Stearns' conjecture holds strictly for first- and second-order filters, it is not true in general, and that an additional restriction introduced by Soderstrom is needed for unimodality of the error surface. The adverse effect of overparameterization which can have serious practical implications is shown through an example. Also, it is shown that for certain insufficient order filters, a nonminimum phase characteristic is sufficient for multimodality of the error surface when the unknown system is driven by white noise. A convenient method for finding the stationary points is introduced.","abstract_html":"Characteristics of the mean-square error surface in adaptive digital filters determine how well a gradient algorithm performs within a given filter structure, i.e., if the surface has steep slopes and contains local minima, a gradient algorithm will have difficulty reaching the global minimum. It is shown that although Stearns' conjecture holds strictly for first- and second-order filters, it is not true in general, and that an additional restriction introduced by Soderstrom is needed for unimodality of the error surface. The adverse effect of overparameterization which can have serious practical implications is shown through an example. Also, it is shown that for certain insufficient order filters, a nonminimum phase characteristic is sufficient for multimodality of the error surface when the unknown system is driven by white noise. A convenient method for finding the stationary points is introduced.","abstract_has_math":false,"creators":["Nayeri, Majid"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-15T19:05:35Z","date_published":"2014-12-15T19:05:35Z","updated_at":"2026-07-22T22:26:00Z","subjects":["Engineering, Electronics and Electrical"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8823211"],"render_values":[{"text":"(UMI)AAI8823211","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/69400","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Nayeri, Majid"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-15T19:05:35Z","10000-01-01","1988"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Electronics and Electrical"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/69400","(UMI)AAI8823211"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Characteristics of the mean-square error surface in adaptive digital filters determine how well a gradient algorithm performs within a given filter structure, i.e., if the surface has steep slopes and contains local minima, a gradient algorithm will have difficulty reaching the global minimum. It is shown that although Stearns' conjecture holds strictly for first- and second-order filters, it is not true in general, and that an additional restriction introduced by Soderstrom is needed for unimodality of the error surface. The adverse effect of overparameterization which can have serious practical implications is shown through an example. Also, it is shown that for certain insufficient order filters, a nonminimum phase characteristic is sufficient for multimodality of the error surface when the unknown system is driven by white noise. A convenient method for finding the stationary points is introduced.","It is also shown how different filter structures of an adaptive filter leads to a change in the characteristics of the corresponding error surface, and consequently, to a change in the corresponding convergence rate and minimum mean square error. A general theory, based on an analysis of stationary points, is presented which shows that whenever a direct form IIR filter with a unimodal MSE surface is transformed into an alternate realization, the MSE surface associated with the new structure may have additional points, which are either new equivalent minima, or saddle points. The general theory is specialized for the parallel and cascade forms.","Made available in DSpace on 2014-12-15T19:05:35Z (GMT). No. of bitstreams: 1 8823211.pdf: 2641790 bytes, checksum: 666dae6c5ee9dfdf8750d75200ff45f1 (MD5) Previous issue date: 1988","Embargo set by: Seth Robbins for item 69566 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","89 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1988."]},{"key":"dc:title","label":"Title","values":["Improvements in Adaptive IIR Filtering: Theory and Application"]}]}],"canonical_facts":{"dc:creator":["Nayeri, Majid"],"dc:date":["2014-12-15T19:05:35Z","10000-01-01","1988"],"dc:description":["Characteristics of the mean-square error surface in adaptive digital filters determine how well a gradient algorithm performs within a given filter structure, i.e., if the surface has steep slopes and contains local minima, a gradient algorithm will have difficulty reaching the global minimum. It is shown that although Stearns' conjecture holds strictly for first- and second-order filters, it is not true in general, and that an additional restriction introduced by Soderstrom is needed for unimodality of the error surface. The adverse effect of overparameterization which can have serious practical implications is shown through an example. Also, it is shown that for certain insufficient order filters, a nonminimum phase characteristic is sufficient for multimodality of the error surface when the unknown system is driven by white noise. A convenient method for finding the stationary points is introduced.","It is also shown how different filter structures of an adaptive filter leads to a change in the characteristics of the corresponding error surface, and consequently, to a change in the corresponding convergence rate and minimum mean square error. A general theory, based on an analysis of stationary points, is presented which shows that whenever a direct form IIR filter with a unimodal MSE surface is transformed into an alternate realization, the MSE surface associated with the new structure may have additional points, which are either new equivalent minima, or saddle points. The general theory is specialized for the parallel and cascade forms.","Made available in DSpace on 2014-12-15T19:05:35Z (GMT). No. of bitstreams: 1 8823211.pdf: 2641790 bytes, checksum: 666dae6c5ee9dfdf8750d75200ff45f1 (MD5) Previous issue date: 1988","Embargo set by: Seth Robbins for item 69566 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","89 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1988."],"dc:identifier":["http://hdl.handle.net/2142/69400","(UMI)AAI8823211"],"dc:subject":["Engineering, Electronics and Electrical"],"dc:title":["Improvements in Adaptive IIR Filtering: Theory and Application"],"dc:type":["text"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:00Z"}