{"id":"https://openalex.org/W2908131432","doi":"https://doi.org/10.1109/iecon.2018.8591107","title":"RBF Neural Networks Modeling Methodology Compared to Non-Parametric Auto-Associative Models for Condition Monitoring Applications","display_name":"RBF Neural Networks Modeling Methodology Compared to Non-Parametric Auto-Associative Models for Condition Monitoring Applications","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2908131432","doi":"https://doi.org/10.1109/iecon.2018.8591107","mag":"2908131432"},"language":"en","primary_location":{"id":"doi:10.1109/iecon.2018.8591107","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2018.8591107","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048312548","display_name":"Marco Aurelio Duarte Alves","orcid":null},"institutions":[{"id":"https://openalex.org/I122558511","display_name":"Universidade Federal de Mato Grosso do Sul","ror":"https://ror.org/0366d2847","country_code":"BR","type":"education","lineage":["https://openalex.org/I122558511"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marco Aurelio Duarte Alves","raw_affiliation_strings":["Federal University of Mato G. do Sul, Campo Grande, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Mato G. do Sul, Campo Grande, Brazil","institution_ids":["https://openalex.org/I122558511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006101738","display_name":"Luigi Galotto","orcid":"https://orcid.org/0000-0002-6307-2015"},"institutions":[{"id":"https://openalex.org/I122558511","display_name":"Universidade Federal de Mato Grosso do Sul","ror":"https://ror.org/0366d2847","country_code":"BR","type":"education","lineage":["https://openalex.org/I122558511"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luigi Galotto","raw_affiliation_strings":["Federal University of Mato G. do Sul, Campo Grande, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Mato G. do Sul, Campo Grande, Brazil","institution_ids":["https://openalex.org/I122558511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026395860","display_name":"Jo\u00e3o Onofre Pereira Pinto","orcid":"https://orcid.org/0000-0002-6547-0607"},"institutions":[{"id":"https://openalex.org/I122558511","display_name":"Universidade Federal de Mato Grosso do Sul","ror":"https://ror.org/0366d2847","country_code":"BR","type":"education","lineage":["https://openalex.org/I122558511"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joao Onofre Pereira Pinto","raw_affiliation_strings":["Federal University of Mato G. do Sul, Campo Grande, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Mato G. do Sul, Campo Grande, Brazil","institution_ids":["https://openalex.org/I122558511"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Raymundo Cordero Garcia","orcid":null},"institutions":[{"id":"https://openalex.org/I122558511","display_name":"Universidade Federal de Mato Grosso do Sul","ror":"https://ror.org/0366d2847","country_code":"BR","type":"education","lineage":["https://openalex.org/I122558511"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Raymundo Cordero Garcia","raw_affiliation_strings":["Federal University of Mato G. do Sul, Campo Grande, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Mato G. do Sul, Campo Grande, Brazil","institution_ids":["https://openalex.org/I122558511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069469031","display_name":"Herbert Teixeira","orcid":null},"institutions":[{"id":"https://openalex.org/I32393484","display_name":"Petrobras (Brazil)","ror":"https://ror.org/0235kyq22","country_code":"BR","type":"company","lineage":["https://openalex.org/I32393484"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Herbert Teixeira","raw_affiliation_strings":["PETROBRAS, CENPES Research Center, Rio de Janeiro, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PETROBRAS, CENPES Research Center, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I32393484"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071825896","display_name":"M. Campos","orcid":"https://orcid.org/0000-0002-5746-6915"},"institutions":[{"id":"https://openalex.org/I32393484","display_name":"Petrobras (Brazil)","ror":"https://ror.org/0235kyq22","country_code":"BR","type":"company","lineage":["https://openalex.org/I32393484"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Mario C. M. Campos","raw_affiliation_strings":["PETROBRAS, CENPES Research Center, Rio de Janeiro, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PETROBRAS, CENPES Research Center, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I32393484"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5406","last_page":"5411"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12564","display_name":"Sensor Technology and Measurement Systems","score":0.9821000099182129,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7347619533538818},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7214435338973999},{"id":"https://openalex.org/keywords/radial-basis-function","display_name":"Radial basis function","score":0.5749197006225586},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5619445443153381},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5487060546875},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4817383587360382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46942073106765747},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.45669466257095337},{"id":"https://openalex.org/keywords/associative-property","display_name":"Associative property","score":0.43422675132751465},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4212806820869446},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34740304946899414},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09706556797027588}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7347619533538818},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7214435338973999},{"id":"https://openalex.org/C98856871","wikidata":"https://www.wikidata.org/wiki/Q1588488","display_name":"Radial basis function","level":3,"score":0.5749197006225586},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5619445443153381},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5487060546875},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4817383587360382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46942073106765747},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.45669466257095337},{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.43422675132751465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4212806820869446},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34740304946899414},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09706556797027588},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon.2018.8591107","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2018.8591107","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1542619214","https://openalex.org/W1639372303","https://openalex.org/W1981240965","https://openalex.org/W2038574620","https://openalex.org/W2042390921","https://openalex.org/W2130415801","https://openalex.org/W2321074348","https://openalex.org/W2500725962","https://openalex.org/W2589057138","https://openalex.org/W2766057469"],"related_works":["https://openalex.org/W1492794944","https://openalex.org/W2494338568","https://openalex.org/W2770593030","https://openalex.org/W1495042958","https://openalex.org/W3154990682","https://openalex.org/W2560201613","https://openalex.org/W2122678784","https://openalex.org/W1794182708","https://openalex.org/W2171975302","https://openalex.org/W169603398"],"abstract_inverted_index":{"This":[0],"work":[1],"presents":[2],"the":[3,14,18,32,34,42,47,61,77,87,92,109,112,116,129,137,146,153,160,163],"use":[4],"of":[5,31,60,76,97,118,136,145,162],"radial":[6],"basis":[7],"function":[8],"artificial":[9],"neural":[10,78],"network":[11,33,79,107],"to":[12,27,103,128,158],"estimate":[13],"sensors":[15],"readings,":[16],"exploring":[17],"analytical":[19],"redundancy":[20],"via":[21],"auto":[22],"association.":[23],"However,":[24],"in":[25,156],"order":[26,157],"guarantee":[28],"good":[29],"performance":[30,63,117,147],"training":[35,44],"and":[36,72,111,115],"optimization":[37],"process":[38],"was":[39],"modified.":[40],"In":[41],"conventional":[43,110],"algorithm,":[45],"although":[46],"stop":[48],"criteria,":[49],"such":[50],"as":[51,149,151],"summed":[52],"squared":[53],"error,":[54],"is":[55,101,126],"reached,":[56],"one":[57],"or":[58],"more":[59],"individual":[62],"metrics,":[64,148],"including:":[65],"i)":[66],"accuracy;":[67],"ii)":[68],"robustness;":[69],"iii)":[70],"spillover":[71],"iv)":[73],"filtering":[74],"matrix":[75],"may":[80],"not":[81],"be":[82,121,141],"satisfactory.":[83],"The":[84],"paper":[85],"describes":[86],"proposed":[88,164],"algorithm":[89,114],"including":[90],"all":[91],"mathematical":[93],"foundation.":[94],"A":[95],"dataset":[96],"a":[98,105,133],"petroleum":[99],"refinery":[100],"used":[102,127],"train":[104],"RBF":[106],"using":[108],"modified":[113],"both":[119],"will":[120,140],"evaluated.":[122],"Furthermore,":[123],"AAKR":[124],"model":[125],"same":[130],"dataset.":[131],"Finally,":[132],"comparison":[134],"study":[135],"developed":[138],"models":[139],"done":[142],"for":[143,152],"each":[144],"well":[150],"overall":[154],"effectiveness":[155],"demonstrate":[159],"superiority":[161],"approach.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
