{"id":"https://openalex.org/W2552216940","doi":"https://doi.org/10.1109/ijcnn.2016.7727183","title":"Polynomial approximation RAM neuron capable of handling true continuous input variables","display_name":"Polynomial approximation RAM neuron capable of handling true continuous input variables","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2552216940","doi":"https://doi.org/10.1109/ijcnn.2016.7727183","mag":"2552216940"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5049204941","display_name":"Paulo J. L. Adeodato","orcid":"https://orcid.org/0000-0002-0406-2474"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Paulo J. L. Adeodato","raw_affiliation_strings":["Centro de Inform\u00e1tica, Universidade Federal de Pernambuco-UFPE, Recife-PE, Brazil"],"affiliations":[{"raw_affiliation_string":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco-UFPE, Recife-PE, Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024258724","display_name":"Rosalvo Ferreira de Oliveira Neto","orcid":"https://orcid.org/0000-0002-3290-5539"},"institutions":[{"id":"https://openalex.org/I4210121543","display_name":"Universidade do Vale do Sapuca\u00ed","ror":"https://ror.org/025zd8760","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210121543"]},{"id":"https://openalex.org/I59530521","display_name":"Universidade Federal do Vale do S\u00e3o Francisco","ror":"https://ror.org/00devjr72","country_code":"BR","type":"education","lineage":["https://openalex.org/I59530521"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rosalvo F. Oliveira Neto","raw_affiliation_strings":["Engenharia da Computa\u00e7\u00e3o, Universidade Federal do Vale do S\u00e3o Francisco (Univasf), Juazeiro-BA, Brazil"],"affiliations":[{"raw_affiliation_string":"Engenharia da Computa\u00e7\u00e3o, Universidade Federal do Vale do S\u00e3o Francisco (Univasf), Juazeiro-BA, Brazil","institution_ids":["https://openalex.org/I59530521","https://openalex.org/I4210121543"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049204941"],"corresponding_institution_ids":["https://openalex.org/I25112270"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0768933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"76","last_page":"83"},"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/T12676","display_name":"Machine Learning and ELM","score":0.9988999962806702,"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/T10057","display_name":"Face and Expression Recognition","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/subspace-topology","display_name":"Subspace topology","score":0.6147879362106323},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5963348746299744},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5961732864379883},{"id":"https://openalex.org/keywords/tuple","display_name":"Tuple","score":0.5631041526794434},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5505616068840027},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5423522591590881},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.4970898926258087},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4929511547088623},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.457308292388916},{"id":"https://openalex.org/keywords/polynomial","display_name":"Polynomial","score":0.44341588020324707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41021305322647095},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39758387207984924},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3011470437049866},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.22371873259544373},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.15231040120124817},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.11513686180114746}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6147879362106323},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5963348746299744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5961732864379883},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.5631041526794434},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5505616068840027},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5423522591590881},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.4970898926258087},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4929511547088623},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.457308292388916},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.44341588020324707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41021305322647095},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39758387207984924},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3011470437049866},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.22371873259544373},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.15231040120124817},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.11513686180114746},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W103650626","https://openalex.org/W159719520","https://openalex.org/W1535147726","https://openalex.org/W1968990163","https://openalex.org/W1971665073","https://openalex.org/W1995426156","https://openalex.org/W2013101793","https://openalex.org/W2018198423","https://openalex.org/W2027406879","https://openalex.org/W2094704691","https://openalex.org/W2116360511","https://openalex.org/W2133614007","https://openalex.org/W2137983211","https://openalex.org/W2140190241","https://openalex.org/W2151362316","https://openalex.org/W2163139056","https://openalex.org/W2493396713","https://openalex.org/W2766736793","https://openalex.org/W2911964244","https://openalex.org/W6632060580","https://openalex.org/W6680704940","https://openalex.org/W6682610290","https://openalex.org/W6995831674"],"related_works":["https://openalex.org/W2006251942","https://openalex.org/W4245395944","https://openalex.org/W2143551613","https://openalex.org/W2364741597","https://openalex.org/W2138823233","https://openalex.org/W1979740464","https://openalex.org/W1492103595","https://openalex.org/W1864774435","https://openalex.org/W1980381208","https://openalex.org/W2334470145"],"abstract_inverted_index":{"RAM-based":[0,87],"neural":[1,120],"networks,":[2],"despite":[3],"their":[4],"speed":[5],"and":[6,134,182],"hardware":[7],"implementability,":[8],"have":[9],"a":[10,47,67,86,100,117],"strong":[11],"drawback":[12],"in":[13,116],"representing":[14],"continuous":[15,94],"input":[16,63,95,105],"variables":[17,106],"that":[18,129],"affects":[19],"its":[20,80],"performance.":[21],"Usually":[22],"these":[23],"networks":[24],"require":[25],"value":[26],"discretization":[27],"followed":[28],"by":[29,132,179],"binary":[30,35,50,164],"encoding":[31,51],"to":[32,45,73,128],"access":[33],"the":[34,39,61,70,89,104,140,144,150,168,180],"memory":[36],"addresses":[37],"where":[38],"learned":[40],"content":[41],"is":[42,114],"stored.":[43],"Added":[44],"being":[46],"cumbersome":[48],"process,":[49],"does":[52],"not":[53],"usually":[54],"preserve":[55],"monotonic":[56],"distance":[57],"relations":[58],"when":[59],"mapping":[60],"original":[62],"space.":[64],"That":[65],"imposes":[66],"difficulty":[68],"for":[69],"learning":[71,109],"system":[72],"create":[74],"basins":[75],"of":[76,103,143,149],"attraction":[77],"thus":[78],"affecting":[79],"generalization":[81],"power.":[82],"This":[83],"paper":[84],"proposes":[85],"neuron,":[88],"PAN":[90,124],"model,":[91],"with":[92,146,158],"true":[93],"handling":[96],"capability":[97],"which":[98],"performs":[99],"polynomial":[101,141],"approximation":[102,142],"on":[107,161],"supervised":[108],"tasks.":[110],"The":[111,137],"proposed":[112,131],"model":[113],"applied":[115],"single":[118],"layer":[119],"network":[121,138],"architecture":[122],"coined":[123],"n-tuple":[125,151],"classifier":[126,152],"similar":[127],"previously":[130],"Adeodato":[133],"Oliveira":[135],"Neto.":[136],"combines":[139],"neuron":[145],"subspace":[147],"sampling":[148],"under":[153],"competitive":[154],"learning.":[155],"Experimental":[156],"comparison":[157],"10-fold":[159],"cross-validation":[160],"2":[162],"Proben1":[163],"decision":[165],"problems":[166],"against":[167],"MultiLayer":[169],"Perceptron":[170],"shows":[171],"equivalent":[172],"performance":[173],"at":[174],"0.05":[175],"significance":[176],"level":[177],"measured":[178],"AUC_ROC":[181],"Max_KS":[183],"metrics.":[184]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
