{"id":"https://openalex.org/W2998995372","doi":"https://doi.org/10.1177/0142331219888366","title":"An improved fruit fly algorithm-unscented Kalman filter-echo state network method for time series prediction of the network traffic data with noises","display_name":"An improved fruit fly algorithm-unscented Kalman filter-echo state network method for time series prediction of the network traffic data with noises","publication_year":2020,"publication_date":"2020-01-07","ids":{"openalex":"https://openalex.org/W2998995372","doi":"https://doi.org/10.1177/0142331219888366","mag":"2998995372"},"language":"en","primary_location":{"id":"doi:10.1177/0142331219888366","is_oa":false,"landing_page_url":"https://doi.org/10.1177/0142331219888366","pdf_url":null,"source":{"id":"https://openalex.org/S24148485","display_name":"Transactions of the Institute of Measurement and Control","issn_l":"0142-3312","issn":["0142-3312","1477-0369"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions of the Institute of Measurement and Control","raw_type":"journal-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/A5101667161","display_name":"Ying Han","orcid":"https://orcid.org/0000-0001-9510-0722"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Han","raw_affiliation_strings":["Northeastern University, China"],"raw_orcid":"https://orcid.org/0000-0001-9510-0722","affiliations":[{"raw_affiliation_string":"Northeastern University, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043040287","display_name":"Yuanwei Jing","orcid":"https://orcid.org/0000-0002-5460-7620"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanwei Jing","raw_affiliation_strings":["Northeastern University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022299830","display_name":"Georgi M. Dimirovski","orcid":"https://orcid.org/0000-0003-1986-8451"},"institutions":[{"id":"https://openalex.org/I129994210","display_name":"Do\u011fu\u015f University","ror":"https://ror.org/0272rjm42","country_code":"TR","type":"education","lineage":["https://openalex.org/I129994210"]},{"id":"https://openalex.org/I76245029","display_name":"Ss. Cyril and Methodius University in Skopje","ror":"https://ror.org/02wk2vx54","country_code":"MK","type":"education","lineage":["https://openalex.org/I76245029"]}],"countries":["MK","TR"],"is_corresponding":false,"raw_author_name":"Georgi M Dimirovski","raw_affiliation_strings":["Dogus University, Turkey","SS Cyril and Methodius University, Macedonia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dogus University, Turkey","institution_ids":["https://openalex.org/I129994210"]},{"raw_affiliation_string":"SS Cyril and Methodius University, Macedonia","institution_ids":["https://openalex.org/I76245029"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043040287"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":1.5645,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.8645473,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"42","issue":"7","first_page":"1281","last_page":"1293"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":1.0,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":1.0,"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/T10232","display_name":"Optical Network Technologies","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10320","display_name":"Neural Networks and Applications","score":0.9918000102043152,"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/kalman-filter","display_name":"Kalman filter","score":0.6764612793922424},{"id":"https://openalex.org/keywords/echo-state-network","display_name":"Echo state network","score":0.6730582118034363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5836541652679443},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5174968242645264},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5170872211456299},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.45366695523262024},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4435378313064575},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4131622016429901},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.36682695150375366},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35386332869529724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2825939357280731},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18962699174880981},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17622491717338562},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15714406967163086},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15691086649894714},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.11562791466712952}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.6764612793922424},{"id":"https://openalex.org/C172025690","wikidata":"https://www.wikidata.org/wiki/Q5332763","display_name":"Echo state network","level":4,"score":0.6730582118034363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5836541652679443},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5174968242645264},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5170872211456299},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.45366695523262024},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4435378313064575},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4131622016429901},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36682695150375366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35386332869529724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2825939357280731},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18962699174880981},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17622491717338562},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15714406967163086},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15691086649894714},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.11562791466712952},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/0142331219888366","is_oa":false,"landing_page_url":"https://doi.org/10.1177/0142331219888366","pdf_url":null,"source":{"id":"https://openalex.org/S24148485","display_name":"Transactions of the Institute of Measurement and Control","issn_l":"0142-3312","issn":["0142-3312","1477-0369"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions of the Institute of Measurement and Control","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4448641170","display_name":null,"funder_award_id":"61773108","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1024003010","https://openalex.org/W1614186689","https://openalex.org/W1882687204","https://openalex.org/W1989130706","https://openalex.org/W2020934227","https://openalex.org/W2040382413","https://openalex.org/W2075074595","https://openalex.org/W2087594545","https://openalex.org/W2093195672","https://openalex.org/W2118706537","https://openalex.org/W2127261457","https://openalex.org/W2148506461","https://openalex.org/W2272128178","https://openalex.org/W2436776244","https://openalex.org/W2518287600","https://openalex.org/W2564428181","https://openalex.org/W2566500639","https://openalex.org/W2580792538","https://openalex.org/W2597331672","https://openalex.org/W2617425124","https://openalex.org/W2740809933","https://openalex.org/W2759168365","https://openalex.org/W2763375416","https://openalex.org/W2766104864","https://openalex.org/W2775668750","https://openalex.org/W2792482170","https://openalex.org/W2800467185","https://openalex.org/W2803565927","https://openalex.org/W2804114647","https://openalex.org/W3044114568","https://openalex.org/W4200333562","https://openalex.org/W4200411312","https://openalex.org/W4247878399"],"related_works":["https://openalex.org/W3216623288","https://openalex.org/W2351280436","https://openalex.org/W3090936158","https://openalex.org/W57315087","https://openalex.org/W3048028252","https://openalex.org/W2511963278","https://openalex.org/W4386848428","https://openalex.org/W4289260438","https://openalex.org/W2745414642","https://openalex.org/W1607430432"],"abstract_inverted_index":{"With":[0],"the":[1,4,16,20,30,33,39,46,85,88,96,111,121,125,134,142,145,150,182,187,224,227],"complexity":[2],"of":[3,19,29,32,120,124,131,144,203,211,220,226],"network":[5,9,21,23,34,47,72,98,167],"system":[6],"rapidly":[7],"increasing,":[8],"traffic":[10,48,99,168],"prediction":[11,52,89,189],"has":[12],"great":[13],"significance":[14],"for":[15,45,160,179],"safety":[17],"pre-warning":[18],"load,":[22],"management":[24],"and":[25,27,50,69,103,128,133,149,162,181,201,209,213,218],"control,":[26],"improvement":[28,194],"quality":[31],"service.":[35],"In":[36],"this":[37],"paper,":[38],"time":[40],"series":[41],"analysis":[42],"is":[43,74,77,107,138],"used":[44,108],"prediction,":[49],"a":[51],"method":[53,190],"combined":[54],"with":[55,101,171],"an":[56,63,192],"optimized":[57],"unscented":[58],"Kalman":[59],"filter":[60],"(UKF)":[61],"by":[62,79,95,116,195],"improved":[64],"fruit":[65],"fly":[66],"algorithm":[67,137],"(IFOA)":[68],"echo":[70],"state":[71,113,147],"(ESN)":[73],"proposed,":[75],"which":[76,154,222],"named":[78],"IFOA-UKF-ESN.":[80],"The":[81],"researches":[82],"mainly":[83],"solve":[84],"problem":[86],"that":[87,186],"accuracy":[90],"might":[91],"be":[92],"greatly":[93],"affected":[94],"actual":[97,166],"data":[100,169],"unknown":[102,161],"time-varying":[104,163],"noises.":[105],"UKF":[106,156],"to":[109,140],"train":[110],"best":[112],"vector":[114],"(formed":[115],"spectral":[117],"radius,":[118],"scale":[119,123],"reservoir,":[122],"input":[126],"units":[127],"connectivity":[129],"rate)":[130],"ESN;":[132],"proposed":[135,139,188,228],"IFOA":[136],"optimize":[141],"weights":[143],"predicted":[146],"value":[148],"covariance":[151],"in":[152],"UKF,":[153],"makes":[155,191],"have":[157],"adaptive":[158],"ability":[159],"noise.":[164],"Three":[165],"sets":[170],"different":[172],"Gaussian":[173],"white":[174],"noise":[175],"distributions":[176],"are":[177],"constructed":[178],"experiments,":[180],"experimental":[183],"results":[184],"show":[185],"average":[193],"reducing":[196],"at":[197,205,214],"least":[198,206,215],"20.60%,":[199],"43.23%":[200],"41.85%":[202],"RMSE,":[204],"23.66%,":[207],"52.38%":[208],"47.50%":[210],"MAE,":[212],"23.58%,":[216],"52.10%":[217],"47.28%":[219],"MAPE,":[221],"verify":[223],"effectiveness":[225],"method.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
