{"id":"https://openalex.org/W2769921592","doi":"https://doi.org/10.1109/iceee.2017.8108894","title":"Nonlinear system modeling using convolutional neural networks","display_name":"Nonlinear system modeling using convolutional neural networks","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2769921592","doi":"https://doi.org/10.1109/iceee.2017.8108894","mag":"2769921592"},"language":"en","primary_location":{"id":"doi:10.1109/iceee.2017.8108894","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceee.2017.8108894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 14th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","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/A5082344982","display_name":"M\u00e1rio L\u00f3pez","orcid":"https://orcid.org/0000-0002-0733-8851"},"institutions":[{"id":"https://openalex.org/I68368234","display_name":"Center for Research and Advanced Studies of the National Polytechnic Institute","ror":"https://ror.org/009eqmr18","country_code":"MX","type":"facility","lineage":["https://openalex.org/I59361560","https://openalex.org/I68368234"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Mario Lopez","raw_affiliation_strings":["Department of Automatic control, CINVESTAV-IPN, Mexico City, Mexico"],"affiliations":[{"raw_affiliation_string":"Department of Automatic control, CINVESTAV-IPN, Mexico City, Mexico","institution_ids":["https://openalex.org/I68368234"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008201587","display_name":"Wen Yu","orcid":"https://orcid.org/0000-0002-9540-7924"},"institutions":[{"id":"https://openalex.org/I68368234","display_name":"Center for Research and Advanced Studies of the National Polytechnic Institute","ror":"https://ror.org/009eqmr18","country_code":"MX","type":"facility","lineage":["https://openalex.org/I59361560","https://openalex.org/I68368234"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Wen Yu","raw_affiliation_strings":["Department of Automatic control, CINVESTAV-IPN, Mexico City, Mexico"],"affiliations":[{"raw_affiliation_string":"Department of Automatic control, CINVESTAV-IPN, Mexico City, Mexico","institution_ids":["https://openalex.org/I68368234"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5082344982"],"corresponding_institution_ids":["https://openalex.org/I68368234"],"apc_list":null,"apc_paid":null,"fwci":0.9751,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.82641153,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9984999895095825,"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":0.9984999895095825,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8371248841285706},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8191765546798706},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.716315507888794},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.697389543056488},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6454198360443115},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5512462854385376},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5425620079040527},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5408906936645508},{"id":"https://openalex.org/keywords/feedforward-neural-network","display_name":"Feedforward neural network","score":0.5354858040809631},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.513011634349823},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4845694601535797},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.4675637185573578},{"id":"https://openalex.org/keywords/system-identification","display_name":"System identification","score":0.4407426416873932},{"id":"https://openalex.org/keywords/nonlinear-system-identification","display_name":"Nonlinear system identification","score":0.43719732761383057},{"id":"https://openalex.org/keywords/feed-forward","display_name":"Feed forward","score":0.43074190616607666},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4204919934272766},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41983702778816223},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07888558506965637},{"id":"https://openalex.org/keywords/control-engineering","display_name":"Control engineering","score":0.06578749418258667},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06275352835655212}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8371248841285706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8191765546798706},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.716315507888794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.697389543056488},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6454198360443115},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5512462854385376},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5425620079040527},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5408906936645508},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.5354858040809631},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.513011634349823},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4845694601535797},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.4675637185573578},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.4407426416873932},{"id":"https://openalex.org/C22157029","wikidata":"https://www.wikidata.org/wiki/Q17080460","display_name":"Nonlinear system identification","level":4,"score":0.43719732761383057},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.43074190616607666},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4204919934272766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41983702778816223},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07888558506965637},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.06578749418258667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06275352835655212},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iceee.2017.8108894","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceee.2017.8108894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 14th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W169539560","https://openalex.org/W1530262073","https://openalex.org/W1573643143","https://openalex.org/W1986278072","https://openalex.org/W2046664056","https://openalex.org/W2063632045","https://openalex.org/W2071039340","https://openalex.org/W2110798204","https://openalex.org/W2112796928","https://openalex.org/W2117731089","https://openalex.org/W2125085157","https://openalex.org/W2130325614","https://openalex.org/W2133693888","https://openalex.org/W2136922672","https://openalex.org/W2137426585","https://openalex.org/W2143787696","https://openalex.org/W2147800946","https://openalex.org/W2163605009","https://openalex.org/W2168893862","https://openalex.org/W2179352600","https://openalex.org/W2266946669","https://openalex.org/W2477834368","https://openalex.org/W3130265595","https://openalex.org/W4285719527","https://openalex.org/W6606918967","https://openalex.org/W6631660994","https://openalex.org/W6676338569","https://openalex.org/W6676481782","https://openalex.org/W6684191040","https://openalex.org/W6791395273"],"related_works":["https://openalex.org/W2115072676","https://openalex.org/W97768505","https://openalex.org/W4311212821","https://openalex.org/W2918103456","https://openalex.org/W1529660427","https://openalex.org/W2102065768","https://openalex.org/W2080531293","https://openalex.org/W2540883726","https://openalex.org/W4390752998","https://openalex.org/W3005666459"],"abstract_inverted_index":{"With":[0],"the":[1,6,44,52],"benefit":[2],"of":[3,25],"convolutional":[4,7],"operation,":[5],"neural":[8,23,36],"networks":[9],"(CNN)":[10],"has":[11],"been":[12],"successfully":[13],"applied":[14],"in":[15],"classification,":[16],"regression":[17],"and":[18,43,46,72],"time":[19],"series":[20],"modeling.":[21],"For":[22],"modeling":[24],"dynamic":[26,53,81],"systems,":[27],"CNN":[28,64,71],"also":[29],"should":[30],"have":[31],"many":[32],"advantages":[33],"over":[34],"other":[35],"models,":[37],"such":[38],"as":[39],"avoiding":[40],"local":[41],"minima":[42],"noises":[45],"outliers":[47],"affections.":[48],"In":[49],"this":[50],"paper,":[51],"system":[54,61,82],"identification":[55,62,83],"is":[56,65],"addressed":[57],"by":[58],"CNN.":[59],"The":[60,75],"with":[63,86],"divided":[66],"into":[67],"two":[68,87],"cases:":[69],"feedforward":[70],"backpropagation":[73],"training.":[74],"proposed":[76],"deep":[77],"learning":[78],"methods":[79],"for":[80],"are":[84],"validated":[85],"benchmark":[88],"data":[89],"sets.":[90]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
