{"id":"https://openalex.org/W4402353275","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650592","title":"A novel Reservoir Architecture for Periodic Time Series Prediction","display_name":"A novel Reservoir Architecture for Periodic Time Series Prediction","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402353275","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650592"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650592","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://biblio.ugent.be/publication/01JHQNRNT6XVZRCFT95BDVZR9Y/file/01JHQNZNYN93E2MM6GCVRH2CHA.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102667044","display_name":"Zhongju Yuan","orcid":"https://orcid.org/0000-0001-7914-7862"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Zhongju Yuan","raw_affiliation_strings":["Ghent University,WAVES Research Group,Gent,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ghent University,WAVES Research Group,Gent,Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074298006","display_name":"Gera\u00ednt A. Wiggins","orcid":"https://orcid.org/0000-0002-1587-112X"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Geraint Wiggins","raw_affiliation_strings":["Vrije Universiteit Brussel,AI Lab,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vrije Universiteit Brussel,AI Lab,Belgium","institution_ids":["https://openalex.org/I13469542"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069519911","display_name":"Dick Botteldooren","orcid":"https://orcid.org/0000-0002-7756-7238"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Dick Botteldooren","raw_affiliation_strings":["Ghent University,WAVES Research Group,Gent,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ghent University,WAVES Research Group,Gent,Belgium","institution_ids":["https://openalex.org/I32597200"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9998999834060669,"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":0.9998999834060669,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9879999756813049,"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/series","display_name":"Series (stratigraphy)","score":0.6541476845741272},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6282526254653931},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6144258379936218},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5431860685348511},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.25798410177230835},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12049105763435364},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06991523504257202}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6541476845741272},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6282526254653931},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6144258379936218},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5431860685348511},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.25798410177230835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12049105763435364},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06991523504257202},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650592","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:archive.ugent.be:01JHQNRNT6XVZRCFT95BDVZR9Y","is_oa":true,"landing_page_url":"http://hdl.handle.net/1854/LU-01JHQNRNT6XVZRCFT95BDVZR9Y","pdf_url":"https://biblio.ugent.be/publication/01JHQNRNT6XVZRCFT95BDVZR9Y/file/01JHQNZNYN93E2MM6GCVRH2CHA.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9798350359312","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:vubissmart:VUBISSMART:2000:176260","is_oa":false,"landing_page_url":"https://biblio.vub.ac.be/vubir/a-novel-reservoir-architecture-for-periodic-time-series-prediction(cce73162-8a01-45ae-a69e-c2fe94e6f17f).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306402573","display_name":"VUBIR (Vrije Universiteit Brussel)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I13469542","host_organization_name":"Vrije Universiteit Brussel","host_organization_lineage":["https://openalex.org/I13469542"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:archive.ugent.be:01JHQNRNT6XVZRCFT95BDVZR9Y","is_oa":true,"landing_page_url":"http://hdl.handle.net/1854/LU-01JHQNRNT6XVZRCFT95BDVZR9Y","pdf_url":"https://biblio.ugent.be/publication/01JHQNRNT6XVZRCFT95BDVZR9Y/file/01JHQNZNYN93E2MM6GCVRH2CHA.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9798350359312","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.6000000238418579}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321730","display_name":"Fonds Wetenschappelijk Onderzoek","ror":"https://ror.org/03qtxy027"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402353275.pdf"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W147326039","https://openalex.org/W1968707255","https://openalex.org/W2003274998","https://openalex.org/W2036451492","https://openalex.org/W2039024743","https://openalex.org/W2054217036","https://openalex.org/W2059804518","https://openalex.org/W2068407998","https://openalex.org/W2074121977","https://openalex.org/W2092723293","https://openalex.org/W2103179919","https://openalex.org/W2117914928","https://openalex.org/W2159682675","https://openalex.org/W2161803347","https://openalex.org/W2172064003","https://openalex.org/W2177933918","https://openalex.org/W2224233427","https://openalex.org/W2267758623","https://openalex.org/W2608997467","https://openalex.org/W2617293960","https://openalex.org/W2794307780","https://openalex.org/W2901736695","https://openalex.org/W2905778174","https://openalex.org/W2908124316","https://openalex.org/W2963861872","https://openalex.org/W2965572192","https://openalex.org/W2982445161","https://openalex.org/W3034749137","https://openalex.org/W3048083301","https://openalex.org/W3092001737","https://openalex.org/W3114259516","https://openalex.org/W3120556073","https://openalex.org/W3132981808","https://openalex.org/W3194202298","https://openalex.org/W4212764442","https://openalex.org/W4226111270","https://openalex.org/W4281563126","https://openalex.org/W4392135912","https://openalex.org/W6605998512"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,24,91,100,118],"novel":[4],"approach":[5],"to":[6,18,94,108,144],"predicting":[7,44],"periodic":[8],"time":[9],"series":[10],"using":[11],"reservoir":[12,35,132],"computing.":[13],"The":[14,61],"model":[15,62],"is":[16,40],"tailored":[17],"deliver":[19],"precise":[20],"forecasts":[21],"of":[22,47,130],"rhythms,":[23],"crucial":[25],"aspect":[26],"for":[27],"tasks":[28],"such":[29],"as":[30,80],"generating":[31],"musical":[32],"rhythm.":[33,48],"Leveraging":[34],"computing,":[36],"our":[37],"proposed":[38],"method":[39],"ultimately":[41],"oriented":[42],"towards":[43],"human":[45,57],"perception":[46,59],"Our":[49],"network":[50],"accurately":[51],"predicts":[52],"rhythmic":[53,74],"signals":[54],"within":[55],"the":[56,85,131],"frequency":[58],"range.":[60],"architecture":[63],"incorporates":[64],"primary":[65],"and":[66,72,82,98,135],"intermediate":[67],"neurons":[68],"tasked":[69],"with":[70,112],"capturing":[71],"transmitting":[73],"information.":[75],"Two":[76],"parameter":[77],"matrices,":[78],"denoted":[79],"c":[81,96,134],"k,":[83],"regulate":[84],"reservoir\u2019s":[86],"overall":[87],"dynamics.":[88],"We":[89],"propose":[90],"loss":[92],"function":[93],"adapt":[95],"post-training":[97],"introduce":[99],"dynamic":[101],"selection":[102],"(DS)":[103],"mechanism":[104],"that":[105],"adjusts":[106],"k":[107],"focus":[109],"on":[110,117],"areas":[111],"outstanding":[113],"contributions.":[114],"Experimental":[115],"results":[116],"diverse":[119],"test":[120],"set":[121],"showcase":[122],"accurate":[123],"predictions,":[124],"further":[125],"improved":[126],"through":[127],"real-time":[128],"tuning":[129],"via":[133],"k.":[136],"Comparative":[137],"assessments":[138],"highlight":[139],"its":[140],"superior":[141],"performance":[142],"compared":[143],"conventional":[145],"models.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
