{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115470"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115470","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Online and offline training for adaptive neuro-fuzzy inference systems using deep and reinforcement learning with hierarchical rule-base reduction","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_has_math":false,"creators":["Ahn, Woojin"],"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":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["Autonomous Vehicle","Adaptive Neuro-Fuzzy Inference System","Reinforcement Learning","Deep Deterministic Policy Gradient","Fuzzy Controller","Artificial Intelligence","Hierarchical Rule Base Reduction"],"languages":["en","eng"],"rights":["Copyright 2022 Woojin Ahn"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115470","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":["Ahn, Woojin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-25"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Autonomous Vehicle","Adaptive Neuro-Fuzzy Inference System","Reinforcement Learning","Deep Deterministic Policy Gradient","Fuzzy Controller","Artificial Intelligence","Hierarchical Rule Base Reduction"]}]},{"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 2022 Woojin Ahn"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115470"]}]},{"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 2022-11-11 without embargo terms","The student, Woojin Ahn, accepted the attached license on 2022-04-15 at 14:43.","The student, Woojin Ahn, submitted this Thesis for approval on 2022-04-18 at 14:21.","This Thesis was approved for publication on 2022-04-25 at 10:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17710 on 2022-11-11 at 13:15:22","This study successfully implemented an Adaptive Neuro-Fuzzy Inference System (ANFIS) [1] vehicle controller trained online and offline with machine learning, deep learning, and reinforcement learning. It was applied to an autonomous skid steering off-road robot path tracking control, as one of the potential applications for this approach. The ANFIS controller was a fuzzy system transformed into a neural network structure to self train. The fuzzy system is explainable because it uses linguistic variables with a logical rule-base, and the neural network is trainable and directly transforms from the fuzzy system structure. The ANFIS, as an explainable artificial intelligence, is designed as a fuzzy logic based human decision-making model (HDMM) with Fuzzy Relations Control Strategy (FRCS) [2] to dramatically reduce computational time and leverage the advantages of both the fuzzy system and neural network. The ANFIS controller was trained using a dataset collected from the expert system in simulation with offline supervised learning. The controller replicated and improved the behavior of the expert model after the offline training. Also, the ANFIS controller was trained using online reinforcement learning on the actual vehicle while driving, which enabled the controller to train itself without any datasets. The result of the supervised learning showed that the error between the ANFIS controller and the expert system was 9.28%. The result of the ANFIS controller trained using online reinforcement learning showed that the trained ANFIS controller performed over 87% in simulation and 73% on the actual vehicle better than the untrained ANFIS controller on five different test courses."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Online and offline training for adaptive neuro-fuzzy inference systems using deep and reinforcement learning with hierarchical rule-base reduction"]}]}],"canonical_facts":{"dc:contributor":["Norris, William"],"dc:creator":["Ahn, Woojin"],"dc:date":["2022-05","2022-04-25"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Woojin Ahn, accepted the attached license on 2022-04-15 at 14:43.","The student, Woojin Ahn, submitted this Thesis for approval on 2022-04-18 at 14:21.","This Thesis was approved for publication on 2022-04-25 at 10:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17710 on 2022-11-11 at 13:15:22","This study successfully implemented an Adaptive Neuro-Fuzzy Inference System (ANFIS) [1] vehicle controller trained online and offline with machine learning, deep learning, and reinforcement learning. It was applied to an autonomous skid steering off-road robot path tracking control, as one of the potential applications for this approach. The ANFIS controller was a fuzzy system transformed into a neural network structure to self train. The fuzzy system is explainable because it uses linguistic variables with a logical rule-base, and the neural network is trainable and directly transforms from the fuzzy system structure. The ANFIS, as an explainable artificial intelligence, is designed as a fuzzy logic based human decision-making model (HDMM) with Fuzzy Relations Control Strategy (FRCS) [2] to dramatically reduce computational time and leverage the advantages of both the fuzzy system and neural network. The ANFIS controller was trained using a dataset collected from the expert system in simulation with offline supervised learning. The controller replicated and improved the behavior of the expert model after the offline training. Also, the ANFIS controller was trained using online reinforcement learning on the actual vehicle while driving, which enabled the controller to train itself without any datasets. The result of the supervised learning showed that the error between the ANFIS controller and the expert system was 9.28%. The result of the ANFIS controller trained using online reinforcement learning showed that the trained ANFIS controller performed over 87% in simulation and 73% on the actual vehicle better than the untrained ANFIS controller on five different test courses."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115470"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Woojin Ahn"],"dc:subject":["Autonomous Vehicle","Adaptive Neuro-Fuzzy Inference System","Reinforcement Learning","Deep Deterministic Policy Gradient","Fuzzy Controller","Artificial Intelligence","Hierarchical Rule Base Reduction"],"dc:title":["Online and offline training for adaptive neuro-fuzzy inference systems using deep and reinforcement learning with hierarchical rule-base reduction"],"dc:type":["text","Thesis"],"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":"2026-07-22T22:24:54Z"}