{"id":"https://openalex.org/W2734399270","doi":"https://doi.org/10.1109/ijcnn.2017.7966121","title":"Structure optimization of dynamic reservoir ensemble using genetic algorithm","display_name":"Structure optimization of dynamic reservoir ensemble using genetic algorithm","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2734399270","doi":"https://doi.org/10.1109/ijcnn.2017.7966121","mag":"2734399270"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966121","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5053266828","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0003-3400-4555"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Binghamton University, Binghamton, New York, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Binghamton University, Binghamton, New York, USA","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064420132","display_name":"Hsiao-Tien Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hsiao-Tien Fan","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Binghamton University, Binghamton, New York, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Binghamton University, Binghamton, New York, USA","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044439388","display_name":"Zhanpeng Jin","orcid":"https://orcid.org/0000-0002-3020-3736"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhanpeng Jin","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Binghamton University, Binghamton, New York, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Binghamton University, Binghamton, New York, USA","institution_ids":["https://openalex.org/I123946342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5053266828"],"corresponding_institution_ids":["https://openalex.org/I123946342"],"apc_list":null,"apc_paid":null,"fwci":0.195,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.61000749,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"8(9)","issue":null,"first_page":"2193","last_page":"2200"},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9869999885559082,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9725000262260437,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reservoir-computing","display_name":"Reservoir computing","score":0.86830735206604},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7009086012840271},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5826401710510254},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5543622970581055},{"id":"https://openalex.org/keywords/reservoir-simulation","display_name":"Reservoir simulation","score":0.5497317314147949},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.5049324631690979},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4802011251449585},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3087647557258606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19623827934265137},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16198986768722534},{"id":"https://openalex.org/keywords/petroleum-engineering","display_name":"Petroleum engineering","score":0.11811256408691406},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.11779382824897766},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11409413814544678},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10096344351768494}],"concepts":[{"id":"https://openalex.org/C135796866","wikidata":"https://www.wikidata.org/wiki/Q7315328","display_name":"Reservoir computing","level":4,"score":0.86830735206604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7009086012840271},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5826401710510254},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5543622970581055},{"id":"https://openalex.org/C2778668878","wikidata":"https://www.wikidata.org/wiki/Q6380338","display_name":"Reservoir simulation","level":2,"score":0.5497317314147949},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5049324631690979},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4802011251449585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3087647557258606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19623827934265137},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16198986768722534},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.11811256408691406},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.11779382824897766},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11409413814544678},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10096344351768494},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966121","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1537758823","https://openalex.org/W1639032689","https://openalex.org/W1876737393","https://openalex.org/W1988878611","https://openalex.org/W2011975175","https://openalex.org/W2022175477","https://openalex.org/W2029296958","https://openalex.org/W2103179919","https://openalex.org/W2118706537","https://openalex.org/W2125303539","https://openalex.org/W2147107577","https://openalex.org/W2150354929","https://openalex.org/W2154808242","https://openalex.org/W2171306489","https://openalex.org/W2184738581","https://openalex.org/W2552129256","https://openalex.org/W3023540311","https://openalex.org/W6681937716","https://openalex.org/W6685384837"],"related_works":["https://openalex.org/W3192662224","https://openalex.org/W2998821156","https://openalex.org/W131743439","https://openalex.org/W4389072666","https://openalex.org/W2887258823","https://openalex.org/W4300888463","https://openalex.org/W4226454644","https://openalex.org/W2946751191","https://openalex.org/W4288350338","https://openalex.org/W4317361262"],"abstract_inverted_index":{"Reservoir":[0],"computing":[1,33],"has":[2,58],"been":[3,59],"widely":[4],"applied":[5],"in":[6,103],"dynamical":[7],"system":[8],"modeling":[9],"and":[10,42,82,86,124],"solving":[11],"time-dependent":[12],"problems":[13],"at":[14],"low":[15],"computational":[16],"expense.":[17],"However,":[18],"when":[19],"confronting":[20],"some":[21],"complex":[22],"tasks":[23],"that":[24],"exhibit":[25],"multiple":[26,63],"sets":[27],"of":[28,55,79,89],"dynamics,":[29],"the":[30,46,53,84,98,112,120,125],"conventional":[31,121],"reservoir":[32,38,56,73,91,127],"model":[34,75,114,123],"with":[35],"a":[36,70,90,104],"single":[37],"may":[39],"become":[40],"ineffective":[41],"powerless.":[43],"Inspired":[44],"by":[45],"modality-independent":[47],"but":[48],"functionally":[49],"connected":[50],"brain":[51],"regions,":[52],"concept":[54],"ensemble":[57,74,92,128],"proposed":[60,113],"which":[61,76],"contains":[62],"reservoirs.":[64],"In":[65],"this":[66],"paper,":[67],"we":[68],"propose":[69],"new":[71],"dynamic":[72],"is":[77],"capable":[78],"automatically":[80],"adapting":[81],"optimizing":[83],"synaptic":[85],"structural":[87],"plasticity":[88],"towards":[93],"an":[94],"optimal":[95],"performance":[96,117],"using":[97],"genetic":[99],"algorithm.":[100],"As":[101],"shown":[102],"real-life":[105],"time":[106],"series":[107],"application":[108],"-":[109],"temperature":[110],"prediction,":[111],"demonstrates":[115],"superior":[116],"over":[118],"both":[119],"single-reservoir":[122],"static":[126],"model.":[129]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
