{"id":"https://openalex.org/W4400035813","doi":"https://doi.org/10.1109/eais58494.2024.10570027","title":"A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps","display_name":"A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps","publication_year":2024,"publication_date":"2024-05-23","ids":{"openalex":"https://openalex.org/W4400035813","doi":"https://doi.org/10.1109/eais58494.2024.10570027"},"language":"en","primary_location":{"id":"doi:10.1109/eais58494.2024.10570027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eais58494.2024.10570027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","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/A5038448571","display_name":"Omid Orang","orcid":"https://orcid.org/0000-0002-4077-3775"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Omid Orang","raw_affiliation_strings":["Universidade Federal de Minas Gerais,Department of Computer Science,Belo Horizonte,Brazil","Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais,Department of Computer Science,Belo Horizonte,Brazil","institution_ids":["https://openalex.org/I110200422"]},{"raw_affiliation_string":"Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031720384","display_name":"Fabricio J. Erazo-Costa","orcid":"https://orcid.org/0009-0001-5968-959X"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Fabricio J. Erazo-Costa","raw_affiliation_strings":["Universidade Federal de Minas Gerais,Graduate Program in Electrical Engineering,Belo Horizonte,Brazil","Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais,Graduate Program in Electrical Engineering,Belo Horizonte,Brazil","institution_ids":["https://openalex.org/I110200422"]},{"raw_affiliation_string":"Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069331818","display_name":"Petr\u00f4nio C\u00e2ndido de Lima e Silva","orcid":"https://orcid.org/0000-0002-1202-2552"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Petr\u00f4nio C. L. Silva","raw_affiliation_strings":["Federal Institute of Northern Minas Gerais Janu&#x00E1;ria Campus,Brazil"],"affiliations":[{"raw_affiliation_string":"Federal Institute of Northern Minas Gerais Janu&#x00E1;ria Campus,Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061650545","display_name":"Guilherme A. Barreto","orcid":"https://orcid.org/0000-0002-7002-1216"},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Guilherme de Alencar Barreto","raw_affiliation_strings":["Universidade Federal do Cear&#x00E1;,Department of Teleinformatics Engineering,Fortaleza,Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Cear&#x00E1;,Department of Teleinformatics Engineering,Fortaleza,Brazil","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053773659","display_name":"Frederico Gadelha Guimar\u00e3es","orcid":"https://orcid.org/0000-0001-9238-8839"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Frederico Gadelha Guimar\u00e3es","raw_affiliation_strings":["Universidade Federal de Minas Gerais,Department of Computer Science,Belo Horizonte,Brazil","Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais,Department of Computer Science,Belo Horizonte,Brazil","institution_ids":["https://openalex.org/I110200422"]},{"raw_affiliation_string":"Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5038448571"],"corresponding_institution_ids":["https://openalex.org/I110200422"],"apc_list":null,"apc_paid":null,"fwci":1.4548,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.8420686,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.988099992275238,"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":0.988099992275238,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9650999903678894,"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":0.9564999938011169,"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/computer-science","display_name":"Computer science","score":0.722461998462677},{"id":"https://openalex.org/keywords/fuzzy-cognitive-map","display_name":"Fuzzy cognitive map","score":0.703216552734375},{"id":"https://openalex.org/keywords/reservoir-computing","display_name":"Reservoir computing","score":0.5463200211524963},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.508571982383728},{"id":"https://openalex.org/keywords/soft-computing","display_name":"Soft computing","score":0.4247272312641144},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3931124210357666},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3706384301185608},{"id":"https://openalex.org/keywords/neuro-fuzzy","display_name":"Neuro-fuzzy","score":0.31618553400039673},{"id":"https://openalex.org/keywords/fuzzy-control-system","display_name":"Fuzzy control system","score":0.2990236282348633},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12787753343582153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722461998462677},{"id":"https://openalex.org/C5041914","wikidata":"https://www.wikidata.org/wiki/Q5511107","display_name":"Fuzzy cognitive map","level":5,"score":0.703216552734375},{"id":"https://openalex.org/C135796866","wikidata":"https://www.wikidata.org/wiki/Q7315328","display_name":"Reservoir computing","level":4,"score":0.5463200211524963},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.508571982383728},{"id":"https://openalex.org/C140073362","wikidata":"https://www.wikidata.org/wiki/Q738759","display_name":"Soft computing","level":3,"score":0.4247272312641144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3931124210357666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3706384301185608},{"id":"https://openalex.org/C29470771","wikidata":"https://www.wikidata.org/wiki/Q4165150","display_name":"Neuro-fuzzy","level":4,"score":0.31618553400039673},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.2990236282348633},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12787753343582153},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eais58494.2024.10570027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eais58494.2024.10570027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320308239","display_name":"EMI","ror":"https://ror.org/01qstkr73"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W264942966","https://openalex.org/W1979559277","https://openalex.org/W2095224843","https://openalex.org/W2121657501","https://openalex.org/W2135046866","https://openalex.org/W2153733695","https://openalex.org/W2748669035","https://openalex.org/W2785196034","https://openalex.org/W2802161886","https://openalex.org/W2908236755","https://openalex.org/W2923573337","https://openalex.org/W2944288969","https://openalex.org/W3033827217","https://openalex.org/W3034944009","https://openalex.org/W3039730552","https://openalex.org/W3047780863","https://openalex.org/W3082874580","https://openalex.org/W3091604364","https://openalex.org/W3133095310","https://openalex.org/W3207924781","https://openalex.org/W3210818346","https://openalex.org/W4206189171","https://openalex.org/W4296143220","https://openalex.org/W4311536596","https://openalex.org/W4312106306","https://openalex.org/W4314935923","https://openalex.org/W4385075884","https://openalex.org/W6925381457"],"related_works":["https://openalex.org/W3192662224","https://openalex.org/W2998821156","https://openalex.org/W4389072666","https://openalex.org/W2887258823","https://openalex.org/W4300888463","https://openalex.org/W4226454644","https://openalex.org/W2949388105","https://openalex.org/W4288089155","https://openalex.org/W2145301614","https://openalex.org/W2024461600"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,30,44,85,100],"novel":[4],"forecastings":[5],"technique":[6,156],"based":[7],"on":[8],"randomized":[9,20],"fuzzy":[10,34],"cognitive":[11],"maps":[12],"(FCM),":[13],"called":[14],"LRHFCM":[15],"(or":[16,57],"large":[17,58,87],"reservoir":[18,40],"of":[19,46,52,76,95,103,109,136,153],"high-order":[21],"FCM)":[22],"for":[23],"predicting":[24],"univariate":[25],"time":[26,35,111,145],"series.":[27,112],"LR-HFCM":[28,138,155],"is":[29,43,67,79,114,140],"hybrid":[31],"method":[32],"combining":[33],"series":[36,146],"(FTS),":[37],"FCMs,":[38],"and":[39,61,97,127],"computing.":[41],"It":[42,113],"type":[45],"echo":[47],"state":[48],"network":[49],"(ESN)":[50],"consisting":[51],"the":[53,71,81,93,119,131,137,151,154],"input":[54,110],"layer,":[55,60,63],"intermediate":[56],"reservoir)":[59],"output":[62,72],"where":[64],"LASSO":[65],"regression":[66],"applied":[68],"to":[69,105,116,159],"train":[70],"layer.":[73],"The":[74,134,148],"novelty":[75],"this":[77],"approach":[78,139],"that":[80,118],"internal":[82],"layer":[83],"includes":[84],"very":[86],"reservoir,":[88],"considering":[89],"different":[90,107,144],"combinations":[91],"from":[92],"sets":[94],"concepts":[96],"order":[98],"using":[99],"certain":[101],"number":[102],"sub-reservoirs":[104],"capture":[106],"dynamics":[108],"important":[115],"highlight":[117,150],"weights":[120],"within":[121],"each":[122],"sub-reservoir":[123],"are":[124],"chosen":[125],"randomly":[126],"remain":[128],"constant":[129],"throughout":[130],"training":[132],"process.":[133],"validity":[135],"evaluated":[141],"across":[142],"15":[143],"datasets.":[147],"results":[149],"outperformance":[152],"in":[157],"comparison":[158],"various":[160],"baseline":[161],"models.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
