{"id":"https://openalex.org/W4414117125","doi":"https://doi.org/10.1109/fuzz62266.2025.11152244","title":"A new backpropagation method dedicated for learning a max-min neural network","display_name":"A new backpropagation method dedicated for learning a max-min neural network","publication_year":2025,"publication_date":"2025-07-06","ids":{"openalex":"https://openalex.org/W4414117125","doi":"https://doi.org/10.1109/fuzz62266.2025.11152244"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz62266.2025.11152244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11152244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","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/A5085689143","display_name":"Olivier Strauss","orcid":"https://orcid.org/0000-0003-4485-772X"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I19894307","display_name":"Universit\u00e9 de Montpellier","ror":"https://ror.org/051escj72","country_code":"FR","type":"education","lineage":["https://openalex.org/I19894307"]},{"id":"https://openalex.org/I4210101743","display_name":"Laboratoire d'Informatique, de Robotique et de Micro\u00e9lectronique de Montpellier","ror":"https://ror.org/013yean28","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I151295451","https://openalex.org/I19894307","https://openalex.org/I4210101743","https://openalex.org/I4210159245","https://openalex.org/I4405261681"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Olivier Strauss","raw_affiliation_strings":["LIRMM, Univ Montpellier, CNRS,Montpellier,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIRMM, Univ Montpellier, CNRS,Montpellier,France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I19894307","https://openalex.org/I4210101743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036957208","display_name":"Agn\u00e8s Rico","orcid":"https://orcid.org/0000-0001-5233-7180"},"institutions":[{"id":"https://openalex.org/I100532134","display_name":"Lyon 1 Universit\u00e9","ror":"https://ror.org/029brtt94","country_code":"FR","type":"education","lineage":["https://openalex.org/I100532134","https://openalex.org/I203339264"]},{"id":"https://openalex.org/I4210141325","display_name":"Clinique Claude-Bernard","ror":"https://ror.org/036mx0561","country_code":"FR","type":"healthcare","lineage":["https://openalex.org/I4210141325"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Agn\u00e8s Rico","raw_affiliation_strings":["ERIC, Univ Claude Bernard,Lyon,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ERIC, Univ Claude Bernard,Lyon,France","institution_ids":["https://openalex.org/I100532134","https://openalex.org/I4210141325"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053513902","display_name":"Isma\u00efl Baaj","orcid":"https://orcid.org/0000-0001-5135-8924"},"institutions":[{"id":"https://openalex.org/I117841876","display_name":"Universit\u00e9 Paris-Panth\u00e9on-Assas","ror":"https://ror.org/04qb2qm38","country_code":"FR","type":"education","lineage":["https://openalex.org/I117841876"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Isma\u00efl Baaj","raw_affiliation_strings":["LEMMA, Univ Paris-Panthéon-Assas University,Paris,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LEMMA, Univ Paris-Panthéon-Assas University,Paris,France","institution_ids":["https://openalex.org/I117841876"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6397,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.84676301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12135","display_name":"Fuzzy Systems and Optimization","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.8823999762535095},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8083000183105469},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7168999910354614},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6211000084877014},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5354999899864197},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.453900009393692},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.335999995470047},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3118000030517578},{"id":"https://openalex.org/keywords/rprop","display_name":"Rprop","score":0.3111000061035156}],"concepts":[{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.8823999762535095},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8083000183105469},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7168999910354614},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6610999703407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.656499981880188},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6211000084877014},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5354999899864197},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.453900009393692},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33889999985694885},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3215000033378601},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C98359873","wikidata":"https://www.wikidata.org/wiki/Q1320470","display_name":"Rprop","level":5,"score":0.3111000061035156},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C29470771","wikidata":"https://www.wikidata.org/wiki/Q4165150","display_name":"Neuro-fuzzy","level":4,"score":0.3003999888896942},{"id":"https://openalex.org/C2779127903","wikidata":"https://www.wikidata.org/wiki/Q6510194","display_name":"Learning rule","level":3,"score":0.29809999465942383},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C66949984","wikidata":"https://www.wikidata.org/wiki/Q7662043","display_name":"Synaptic weight","level":3,"score":0.2743000090122223},{"id":"https://openalex.org/C177973122","wikidata":"https://www.wikidata.org/wiki/Q7860946","display_name":"Types of artificial neural networks","level":4,"score":0.2734000086784363},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.25920000672340393},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz62266.2025.11152244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11152244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","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":12,"referenced_works":["https://openalex.org/W294994052","https://openalex.org/W1964468250","https://openalex.org/W2005441399","https://openalex.org/W2055576996","https://openalex.org/W2061821032","https://openalex.org/W2088264919","https://openalex.org/W2585745035","https://openalex.org/W2613648727","https://openalex.org/W2891721385","https://openalex.org/W4244888682","https://openalex.org/W4391288516","https://openalex.org/W4391849315"],"related_works":["https://openalex.org/W1584270863","https://openalex.org/W2098962763","https://openalex.org/W2157746493","https://openalex.org/W4390190477","https://openalex.org/W180587397","https://openalex.org/W2085961337","https://openalex.org/W4241378172","https://openalex.org/W2144801536","https://openalex.org/W2371506377","https://openalex.org/W2766802075"],"abstract_inverted_index":{"In":[0],"this":[1,54],"paper,":[2],"we":[3],"propose":[4],"a":[5,81,96,109],"new":[6],"learning":[7,66],"method":[8,67,79,130],"for":[9,21,60,84,134],"max-min":[10,43,76,97,118],"neural":[11,136],"networks":[12,137],"with":[13,30],"more":[14],"than":[15],"three":[16],"layers.":[17],"These":[18],"networks,":[19],"valued":[20],"their":[22,27],"intrinsic":[23],"interpretability":[24],"due":[25],"to":[26,57,74],"direct":[28],"link":[29],"fuzzy":[31],"logic":[32],"if-then":[33],"rules,":[34],"face":[35],"computational":[36],"challenges":[37],"stemming":[38],"from":[39],"the":[40,64,75,91,112,132],"non-linearity":[41],"of":[42,90,111],"operations.":[44],"Unlike":[45],"traditional":[46],"gradient":[47,106],"descent":[48],"approaches,":[49],"which":[50],"are":[51],"ineffective":[52],"in":[53,142],"context":[55],"(owing":[56],"zero":[58],"derivatives":[59],"most":[61],"synaptic":[62,92,113],"weights),":[63],"proposed":[65],"relies":[68],"on":[69,121],"approximate":[70,105],"and":[71,107,123],"stable":[72],"solutions":[73],"equations.":[77],"This":[78,129],"uses":[80],"three-step":[82],"backpropagation":[83,110],"each":[85],"neuron":[86],"layer:":[87],"an":[88,101,104],"update":[89,102],"weights":[93],"by":[94,100,115],"solving":[95,116],"equation":[98],"followed":[99],"using":[103],"then":[108],"values":[114],"another":[117],"equation.":[119],"Experiments":[120],"synthetic":[122],"real-world":[124],"data":[125],"show":[126],"promising":[127],"performance.":[128],"paves":[131],"way":[133],"interpretable":[135],"that":[138],"can":[139],"be":[140],"used":[141],"complex":[143],"scenarios.":[144]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}