{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124327"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124327","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Adaptive neuro-fuzzy inference system based neural network and parameter constraints","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Juston, Marius Francois Robert"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Norris, William"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Anfis","Optimization","Fuzzy Logic","Parameter Constraint","Ddpg"],"languages":["en","eng"],"rights":["Copyright 2024 Marius Juston"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124327","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Norris, William"]},{"key":"dc:creator","label":"Author","values":["Juston, Marius Francois Robert"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-05-01"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems & Entrepreneurial Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Anfis","Optimization","Fuzzy Logic","Parameter Constraint","Ddpg"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Marius Juston"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124327"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Marius Juston, accepted the attached license on 2024-04-19 at 10:51.","The student, Marius Juston, submitted this Thesis for approval on 2024-04-19 at 10:59.","This Thesis was approved for publication on 2024-05-01 at 10:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20477 on 2024-09-16 at 00:35:22","This thesis presents a comprehensive approach to designing and optimizing an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for robotics applications. This research investigates the implementation of constraints on the input and the output linguistic variables, such as single-sided and symmetry constraints, with theoretical guarantees to maintain the desired constraint for offline and training purposes. Specifically, the linguistic joint membership functions that underlie the ANFIS are defined, focusing on symmetrical inputs/outputs and jointly optimized trapezoid membership functions. A novel way of representing and computing the Mamdani ruleset was generated and explored. These constraints aim to reduce the number of training parameters and thus increase training speed. Further optimizations for the ANFIS were derived based on design assumptions, including training the membership functions on closed or single-sided domains and improving the training of linear-based membership functions using a softplus-based saturation function. The relationship between the ANFIS and radial basis functions, as a special case of the neural network definition, was expanded and derived. This helped demonstrate the ANFIS network as a universal approximator. The ANFIS's use was examined in applying line following for a differential drive model, where its stability was explored. The optimal output membership weights based on mean square error optimization were also symbolically obtained for offline training. Additional training of the ANFIS's input/output membership functions was performed using the DDPG (Deep Deterministic Policy Gradient) algorithm to train the ANFIS system to run online."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Adaptive neuro-fuzzy inference system based neural network and parameter constraints"]}]}],"canonical_facts":{"dc:contributor":["Norris, William"],"dc:creator":["Juston, Marius Francois Robert"],"dc:date":["2024-05","2024-05-01"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Marius Juston, accepted the attached license on 2024-04-19 at 10:51.","The student, Marius Juston, submitted this Thesis for approval on 2024-04-19 at 10:59.","This Thesis was approved for publication on 2024-05-01 at 10:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20477 on 2024-09-16 at 00:35:22","This thesis presents a comprehensive approach to designing and optimizing an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for robotics applications. This research investigates the implementation of constraints on the input and the output linguistic variables, such as single-sided and symmetry constraints, with theoretical guarantees to maintain the desired constraint for offline and training purposes. Specifically, the linguistic joint membership functions that underlie the ANFIS are defined, focusing on symmetrical inputs/outputs and jointly optimized trapezoid membership functions. A novel way of representing and computing the Mamdani ruleset was generated and explored. These constraints aim to reduce the number of training parameters and thus increase training speed. Further optimizations for the ANFIS were derived based on design assumptions, including training the membership functions on closed or single-sided domains and improving the training of linear-based membership functions using a softplus-based saturation function. The relationship between the ANFIS and radial basis functions, as a special case of the neural network definition, was expanded and derived. This helped demonstrate the ANFIS network as a universal approximator. The ANFIS's use was examined in applying line following for a differential drive model, where its stability was explored. The optimal output membership weights based on mean square error optimization were also symbolically obtained for offline training. Additional training of the ANFIS's input/output membership functions was performed using the DDPG (Deep Deterministic Policy Gradient) algorithm to train the ANFIS system to run online."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124327"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Marius Juston"],"dc:subject":["Anfis","Optimization","Fuzzy Logic","Parameter Constraint","Ddpg"],"dc:title":["Adaptive neuro-fuzzy inference system based neural network and parameter constraints"],"dc:type":["text"],"thesis:degree_discipline":["Systems & Entrepreneurial Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}