Reinforcement Self-Organizing Interval Type-2 Fuzzy System with ant colony optimization

This paper proposes a Reinforcement Self-Organizing Interval Type-2 Fuzzy System with Ant Colony Optimization (RSOIT2FS-ACO) method. The antecedent part in each fuzzy rule of the RSOIT2FS-ACO uses interval type-2 fuzzy sets in order to improve system robustness to noise. There are no fuzzy rules ini...

Full description

Saved in:
Bibliographic Details
Main Authors: Chia-Feng Juang, Chia-Hung Hsu, Chia-Feng Chuang
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Ant colony optimization
Cybernetics
Frequency selective surfaces
Fuzzy control
Fuzzy sets
Fuzzy systems
Noise robustness
reinforcement learning
Supervised learning
type-2 fuzzy systems
USA Councils
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 776
container_issue
container_start_page 771
container_title
container_volume
creator Chia-Feng Juang
Chia-Hung Hsu
Chia-Feng Chuang
description This paper proposes a Reinforcement Self-Organizing Interval Type-2 Fuzzy System with Ant Colony Optimization (RSOIT2FS-ACO) method. The antecedent part in each fuzzy rule of the RSOIT2FS-ACO uses interval type-2 fuzzy sets in order to improve system robustness to noise. There are no fuzzy rules initially. The RSOIT2FS-ACO generates all rules online. The consequent part of each fuzzy rule is designed using Ant Colony Optimization (ACO). The ACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails. The RSOIT2FS-ACO method is applied to a truck backing control. The proposed RSOIT2FS-ACO is compared with other reinforcement fuzzy systems to verify its efficiency and effectiveness. A comparison with type-1 fuzzy systems verifies the robustness of using type-2 fuzzy systems to noise.
doi_str_mv 10.1109/ICSMC.2009.5346806
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_5346806</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5346806</ieee_id><sourcerecordid>5346806</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-54b91e82a9d968877cfc0534a0e50e157dc7e91b3bc8382a815a0038cb8429393</originalsourceid><addsrcrecordid>eNpVkMtOwzAURM2jEm3pD8DGP5By_YrtJYooVCqqRItgVznpTTHKo0oMKPl6ItENq1nMzJFmCLlhMGcM7N0y2Twncw5g50rI2EB8RmZWGya5lFxbac_JmCutIxYrdfHPE_ySjBnEPLKcv4_IZMAYC_FAuSKTtv0E4CCZGZO3F_RVXjcZllgFusEij9bNwVW-99WBLquAzbcr6LY7YsTp4qvvO7rp2oAl_fHhg7qhldVFXXW0PgZf-t4FX1fXZJS7osXZSafkdfGwTZ6i1fpxmdyvIs-0CpGSqWVouLN7GxujdZZnMMx1gAqQKb3PNFqWijQzYogZphyAMFlqJLfCiim5_eN6RNwdG1-6ptudDhO_CF5Y5Q</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Reinforcement Self-Organizing Interval Type-2 Fuzzy System with ant colony optimization</title><source>IEEE Xplore All Conference Series</source><creator>Chia-Feng Juang ; Chia-Hung Hsu ; Chia-Feng Chuang</creator><creatorcontrib>Chia-Feng Juang ; Chia-Hung Hsu ; Chia-Feng Chuang</creatorcontrib><description>This paper proposes a Reinforcement Self-Organizing Interval Type-2 Fuzzy System with Ant Colony Optimization (RSOIT2FS-ACO) method. The antecedent part in each fuzzy rule of the RSOIT2FS-ACO uses interval type-2 fuzzy sets in order to improve system robustness to noise. There are no fuzzy rules initially. The RSOIT2FS-ACO generates all rules online. The consequent part of each fuzzy rule is designed using Ant Colony Optimization (ACO). The ACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails. The RSOIT2FS-ACO method is applied to a truck backing control. The proposed RSOIT2FS-ACO is compared with other reinforcement fuzzy systems to verify its efficiency and effectiveness. A comparison with type-1 fuzzy systems verifies the robustness of using type-2 fuzzy systems to noise.</description><identifier>ISSN: 1062-922X</identifier><identifier>ISBN: 9781424427932</identifier><identifier>ISBN: 1424427932</identifier><identifier>EISSN: 2577-1655</identifier><identifier>EISBN: 9781424427949</identifier><identifier>EISBN: 1424427940</identifier><identifier>DOI: 10.1109/ICSMC.2009.5346806</identifier><identifier>LCCN: 2008906680</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Ant colony optimization ; Cybernetics ; Frequency selective surfaces ; Fuzzy control ; Fuzzy sets ; Fuzzy systems ; Noise robustness ; reinforcement learning ; Supervised learning ; type-2 fuzzy systems ; USA Councils</subject><ispartof>2009 IEEE International Conference on Systems, Man and Cybernetics, 2009, p.771-776</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5346806$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5346806$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chia-Feng Juang</creatorcontrib><creatorcontrib>Chia-Hung Hsu</creatorcontrib><creatorcontrib>Chia-Feng Chuang</creatorcontrib><title>Reinforcement Self-Organizing Interval Type-2 Fuzzy System with ant colony optimization</title><title>2009 IEEE International Conference on Systems, Man and Cybernetics</title><addtitle>ICSMC</addtitle><description>This paper proposes a Reinforcement Self-Organizing Interval Type-2 Fuzzy System with Ant Colony Optimization (RSOIT2FS-ACO) method. The antecedent part in each fuzzy rule of the RSOIT2FS-ACO uses interval type-2 fuzzy sets in order to improve system robustness to noise. There are no fuzzy rules initially. The RSOIT2FS-ACO generates all rules online. The consequent part of each fuzzy rule is designed using Ant Colony Optimization (ACO). The ACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails. The RSOIT2FS-ACO method is applied to a truck backing control. The proposed RSOIT2FS-ACO is compared with other reinforcement fuzzy systems to verify its efficiency and effectiveness. A comparison with type-1 fuzzy systems verifies the robustness of using type-2 fuzzy systems to noise.</description><subject>Algorithm design and analysis</subject><subject>Ant colony optimization</subject><subject>Cybernetics</subject><subject>Frequency selective surfaces</subject><subject>Fuzzy control</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>Noise robustness</subject><subject>reinforcement learning</subject><subject>Supervised learning</subject><subject>type-2 fuzzy systems</subject><subject>USA Councils</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>9781424427932</isbn><isbn>1424427932</isbn><isbn>9781424427949</isbn><isbn>1424427940</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVkMtOwzAURM2jEm3pD8DGP5By_YrtJYooVCqqRItgVznpTTHKo0oMKPl6ItENq1nMzJFmCLlhMGcM7N0y2Twncw5g50rI2EB8RmZWGya5lFxbac_JmCutIxYrdfHPE_ySjBnEPLKcv4_IZMAYC_FAuSKTtv0E4CCZGZO3F_RVXjcZllgFusEij9bNwVW-99WBLquAzbcr6LY7YsTp4qvvO7rp2oAl_fHhg7qhldVFXXW0PgZf-t4FX1fXZJS7osXZSafkdfGwTZ6i1fpxmdyvIs-0CpGSqWVouLN7GxujdZZnMMx1gAqQKb3PNFqWijQzYogZphyAMFlqJLfCiim5_eN6RNwdG1-6ptudDhO_CF5Y5Q</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Chia-Feng Juang</creator><creator>Chia-Hung Hsu</creator><creator>Chia-Feng Chuang</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200910</creationdate><title>Reinforcement Self-Organizing Interval Type-2 Fuzzy System with ant colony optimization</title><author>Chia-Feng Juang ; Chia-Hung Hsu ; Chia-Feng Chuang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-54b91e82a9d968877cfc0534a0e50e157dc7e91b3bc8382a815a0038cb8429393</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithm design and analysis</topic><topic>Ant colony optimization</topic><topic>Cybernetics</topic><topic>Frequency selective surfaces</topic><topic>Fuzzy control</topic><topic>Fuzzy sets</topic><topic>Fuzzy systems</topic><topic>Noise robustness</topic><topic>reinforcement learning</topic><topic>Supervised learning</topic><topic>type-2 fuzzy systems</topic><topic>USA Councils</topic><toplevel>online_resources</toplevel><creatorcontrib>Chia-Feng Juang</creatorcontrib><creatorcontrib>Chia-Hung Hsu</creatorcontrib><creatorcontrib>Chia-Feng Chuang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chia-Feng Juang</au><au>Chia-Hung Hsu</au><au>Chia-Feng Chuang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Reinforcement Self-Organizing Interval Type-2 Fuzzy System with ant colony optimization</atitle><btitle>2009 IEEE International Conference on Systems, Man and Cybernetics</btitle><stitle>ICSMC</stitle><date>2009-10</date><risdate>2009</risdate><spage>771</spage><epage>776</epage><pages>771-776</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>9781424427932</isbn><isbn>1424427932</isbn><eisbn>9781424427949</eisbn><eisbn>1424427940</eisbn><abstract>This paper proposes a Reinforcement Self-Organizing Interval Type-2 Fuzzy System with Ant Colony Optimization (RSOIT2FS-ACO) method. The antecedent part in each fuzzy rule of the RSOIT2FS-ACO uses interval type-2 fuzzy sets in order to improve system robustness to noise. There are no fuzzy rules initially. The RSOIT2FS-ACO generates all rules online. The consequent part of each fuzzy rule is designed using Ant Colony Optimization (ACO). The ACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails. The RSOIT2FS-ACO method is applied to a truck backing control. The proposed RSOIT2FS-ACO is compared with other reinforcement fuzzy systems to verify its efficiency and effectiveness. A comparison with type-1 fuzzy systems verifies the robustness of using type-2 fuzzy systems to noise.</abstract><pub>IEEE</pub><doi>10.1109/ICSMC.2009.5346806</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1062-922X
ispartof 2009 IEEE International Conference on Systems, Man and Cybernetics, 2009, p.771-776
issn 1062-922X
2577-1655
language eng
recordid cdi_ieee_primary_5346806
source IEEE Xplore All Conference Series
subjects Algorithm design and analysis
Ant colony optimization
Cybernetics
Frequency selective surfaces
Fuzzy control
Fuzzy sets
Fuzzy systems
Noise robustness
reinforcement learning
Supervised learning
type-2 fuzzy systems
USA Councils
title Reinforcement Self-Organizing Interval Type-2 Fuzzy System with ant colony optimization
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T06%3A42%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Reinforcement%20Self-Organizing%20Interval%20Type-2%20Fuzzy%20System%20with%20ant%20colony%20optimization&rft.btitle=2009%20IEEE%20International%20Conference%20on%20Systems,%20Man%20and%20Cybernetics&rft.au=Chia-Feng%20Juang&rft.date=2009-10&rft.spage=771&rft.epage=776&rft.pages=771-776&rft.issn=1062-922X&rft.eissn=2577-1655&rft.isbn=9781424427932&rft.isbn_list=1424427932&rft_id=info:doi/10.1109/ICSMC.2009.5346806&rft.eisbn=9781424427949&rft.eisbn_list=1424427940&rft_dat=%3Cieee_CHZPO%3E5346806%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-54b91e82a9d968877cfc0534a0e50e157dc7e91b3bc8382a815a0038cb8429393%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5346806&rfr_iscdi=true