{"id":"https://openalex.org/W4295768100","doi":"https://doi.org/10.1109/fuzz-ieee55066.2022.9882672","title":"Saturation in Fuzzy Flip-Flop Neural Networks","display_name":"Saturation in Fuzzy Flip-Flop Neural Networks","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4295768100","doi":"https://doi.org/10.1109/fuzz-ieee55066.2022.9882672"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz-ieee55066.2022.9882672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee55066.2022.9882672","pdf_url":null,"source":{"id":"https://openalex.org/S4363608205","display_name":"2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"},"type":"article","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/A5103129777","display_name":"Piotr A. Kowalski","orcid":"https://orcid.org/0000-0003-4041-6900"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Piotr A. Kowalski","raw_affiliation_strings":["AGH University of Science and Technology,Faculty of Physics and Applied Computer Science,Krak&#x00F3;w,Poland,30-059"],"affiliations":[{"raw_affiliation_string":"AGH University of Science and Technology,Faculty of Physics and Applied Computer Science,Krak&#x00F3;w,Poland,30-059","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085635790","display_name":"Tomasz Sloczynski","orcid":null},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Tomasz Sloczynski","raw_affiliation_strings":["AGH University of Science and Technology,Faculty of Physics and Applied Computer Science,Krak&#x00F3;w,Poland,30-059"],"affiliations":[{"raw_affiliation_string":"AGH University of Science and Technology,Faculty of Physics and Applied Computer Science,Krak&#x00F3;w,Poland,30-059","institution_ids":["https://openalex.org/I686019"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103129777"],"corresponding_institution_ids":["https://openalex.org/I686019"],"apc_list":null,"apc_paid":null,"fwci":0.2079,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.41203404,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9998000264167786,"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/T10320","display_name":"Neural Networks and Applications","score":0.9998000264167786,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9991000294685364,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9739000201225281,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.722257137298584},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.663508415222168},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.476860374212265},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.4708665907382965},{"id":"https://openalex.org/keywords/saturation","display_name":"Saturation (graph theory)","score":0.464275062084198},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.46208450198173523},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3896626830101013},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3363182842731476},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19357609748840332}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.722257137298584},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.663508415222168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.476860374212265},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.4708665907382965},{"id":"https://openalex.org/C9930424","wikidata":"https://www.wikidata.org/wiki/Q7426587","display_name":"Saturation (graph theory)","level":2,"score":0.464275062084198},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.46208450198173523},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3896626830101013},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3363182842731476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19357609748840332},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz-ieee55066.2022.9882672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee55066.2022.9882672","pdf_url":null,"source":{"id":"https://openalex.org/S4363608205","display_name":"2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W147307174","https://openalex.org/W151192688","https://openalex.org/W982563554","https://openalex.org/W1483055888","https://openalex.org/W1550703529","https://openalex.org/W1599746992","https://openalex.org/W1875348914","https://openalex.org/W1975783225","https://openalex.org/W1999205703","https://openalex.org/W2047702131","https://openalex.org/W2093809744","https://openalex.org/W2135230399","https://openalex.org/W2141263480","https://openalex.org/W2141803445","https://openalex.org/W2151818015","https://openalex.org/W2152195021","https://openalex.org/W2162506329","https://openalex.org/W2168832627","https://openalex.org/W2240288625","https://openalex.org/W2275584500","https://openalex.org/W2339385604","https://openalex.org/W2553286395","https://openalex.org/W2984376566","https://openalex.org/W3013263853","https://openalex.org/W3014256346","https://openalex.org/W3120740533","https://openalex.org/W4226070509","https://openalex.org/W4246565613","https://openalex.org/W6606169240","https://openalex.org/W6695167088","https://openalex.org/W6703797334","https://openalex.org/W6810794439"],"related_works":["https://openalex.org/W3008339103","https://openalex.org/W2404647514","https://openalex.org/W1667647204","https://openalex.org/W4247536566","https://openalex.org/W3119814709","https://openalex.org/W2018477250","https://openalex.org/W1508895727","https://openalex.org/W4241418540","https://openalex.org/W2725786787","https://openalex.org/W4283160672"],"abstract_inverted_index":{"The":[0,36,183],"aim":[1,184],"of":[2,10,28,43,49,61,64,68,106,109,123,132,138,146,185,188,197,205,213],"this":[3,34,40],"paper":[4],"is":[5,192],"to":[6,24,32,85,95,116,193],"investigate":[7],"the":[8,29,47,53,62,69,73,86,92,96,107,112,118,121,130,133,136,139,147,157,177,186,189,195,198,203,211,221],"phenomenon":[9,208],"saturation":[11,51,79,108,154,204],"in":[12,52,144,201,220],"a":[13,26,207],"recurrent":[14],"neural":[15,70,149],"network":[16,55,71,150],"based":[17],"on":[18,38,78,129],"J-K":[19],"Fuzzy":[20],"Flip-Flop":[21],"neurons,":[22,206],"and":[23,58,72,153,172,215],"develop":[25],"modification":[27],"learning":[30,87,98,151,181],"process":[31],"reduce":[33],"phenomenon.":[35],"work":[37],"solving":[39],"problem":[41],"consists":[42],"several":[44],"stages.":[45],"Firstly,":[46],"concept":[48],"neuron":[50],"FFF-type":[54],"was":[56,81,126,142],"defined":[57],"an":[59],"analysis":[60],"influence":[63],"basic":[65,97],"internal":[66],"parameters":[67],"Particle":[74],"Swarm":[75],"Optimization":[76],"algorithm":[77,88,99],"reduction":[80,105],"performed.":[82],"Next,":[83],"modifications":[84],"were":[89,100,164],"investigated.":[90],"In":[91,114],"paper,":[93],"extensions":[94],"then":[101],"presented.":[102],"These":[103],"allowed":[104],"neurons":[110],"inside":[111],"network.":[113],"order":[115],"perform":[117],"above":[119],"analysis,":[120,159],"issue":[122],"data":[124,162],"classification":[125],"addressed":[127],"and,":[128],"basis":[131],"obtained":[134],"results,":[135],"effectiveness":[137],"introduced":[140],"changes":[141],"verified":[143],"terms":[145],"achieved":[148],"errors":[152],"indices.":[155],"For":[156],"numerical":[158],"five":[160],"benchmark":[161],"sets":[163],"exploited:":[165],"Iris,":[166],"Seeds,":[167],"Wine,":[168],"Glass,":[169],"Breast":[170],"Cancer":[171],"Wisconsin":[173],"-":[174],"drawn":[175],"from":[176],"well-known":[178],"UCI":[179],"Machine":[180],"repository.":[182],"validation":[187],"developed":[190],"algorithms":[191],"demonstrate":[194],"correctness":[196],"proposed":[199],"solution":[200],"reducing":[202],"that":[209],"affects":[210],"efficiency":[212],"learning,":[214],"which":[216],"translates":[217],"into":[218],"confidence":[219],"results":[222],"obtained.":[223]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
