{"id":"https://openalex.org/W1902688491","doi":"https://doi.org/10.1109/iscas.2006.1693294","title":"Minimum mean squared error time series classification using an echo state network prediction model","display_name":"Minimum mean squared error time series classification using an echo state network prediction model","publication_year":2006,"publication_date":"2006-09-22","ids":{"openalex":"https://openalex.org/W1902688491","doi":"https://doi.org/10.1109/iscas.2006.1693294","mag":"1902688491"},"language":"en","primary_location":{"id":"doi:10.1109/iscas.2006.1693294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2006.1693294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE International Symposium on Circuits and Systems","raw_type":"proceedings-article"},"type":"conference-paper","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/A5053622159","display_name":"Mark D. Skowronski","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M.D. Skowronski","raw_affiliation_strings":["Computational Neuro-Engineering Laboratory, Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA","Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computational Neuro-Engineering Laboratory, Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL#TAB#","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113319313","display_name":"J.G. Harris","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J.G. Harris","raw_affiliation_strings":["Computational Neuro-Engineering Laboratory, Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA","Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computational Neuro-Engineering Laboratory, Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL#TAB#","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":3.4198,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.93317999,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3153","last_page":"3156"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9990000128746033,"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":0.9984999895095825,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7378916144371033},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7029274702072144},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6587833166122437},{"id":"https://openalex.org/keywords/echo-state-network","display_name":"Echo state network","score":0.6158969402313232},{"id":"https://openalex.org/keywords/viterbi-algorithm","display_name":"Viterbi algorithm","score":0.6085454225540161},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5328844785690308},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5117995142936707},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5058609843254089},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.49433159828186035},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.482316255569458},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.47606438398361206},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4718577563762665},{"id":"https://openalex.org/keywords/linear-prediction","display_name":"Linear prediction","score":0.4490884840488434},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.437095046043396},{"id":"https://openalex.org/keywords/viterbi-decoder","display_name":"Viterbi decoder","score":0.42803120613098145},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42453616857528687},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4135812520980835},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.2719726264476776},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2574431896209717},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.2035771608352661},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17767122387886047},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12453445792198181}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7378916144371033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7029274702072144},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6587833166122437},{"id":"https://openalex.org/C172025690","wikidata":"https://www.wikidata.org/wiki/Q5332763","display_name":"Echo state network","level":4,"score":0.6158969402313232},{"id":"https://openalex.org/C60582962","wikidata":"https://www.wikidata.org/wiki/Q83886","display_name":"Viterbi algorithm","level":3,"score":0.6085454225540161},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5328844785690308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5117995142936707},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5058609843254089},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.49433159828186035},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.482316255569458},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.47606438398361206},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4718577563762665},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.4490884840488434},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.437095046043396},{"id":"https://openalex.org/C117379686","wikidata":"https://www.wikidata.org/wiki/Q6996459","display_name":"Viterbi decoder","level":3,"score":0.42803120613098145},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42453616857528687},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4135812520980835},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2719726264476776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2574431896209717},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2035771608352661},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17767122387886047},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12453445792198181},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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":1,"locations":[{"id":"doi:10.1109/iscas.2006.1693294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2006.1693294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE International Symposium on Circuits and Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.49000000953674316,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1965664780","https://openalex.org/W2002402416","https://openalex.org/W2069976350","https://openalex.org/W2071396781","https://openalex.org/W2079735306","https://openalex.org/W2110871230","https://openalex.org/W2111162116","https://openalex.org/W2112090702","https://openalex.org/W2113640225","https://openalex.org/W2118706537","https://openalex.org/W2120472140","https://openalex.org/W2125838338","https://openalex.org/W2128084896","https://openalex.org/W2135090195","https://openalex.org/W2140755429","https://openalex.org/W2148776466","https://openalex.org/W2150354929","https://openalex.org/W2158010300","https://openalex.org/W2169762136","https://openalex.org/W2798121865","https://openalex.org/W6681937716"],"related_works":["https://openalex.org/W2102309991","https://openalex.org/W1795315578","https://openalex.org/W2373954783","https://openalex.org/W2133857928","https://openalex.org/W2143297499","https://openalex.org/W2535886977","https://openalex.org/W2356694334","https://openalex.org/W2991144886","https://openalex.org/W2790444905","https://openalex.org/W1843778016"],"abstract_inverted_index":{"The":[0,45,217],"echo":[1],"state":[2],"network":[3,14],"(ESN)":[4],"has":[5],"been":[6],"recently":[7],"proposed":[8],"as":[9],"an":[10,70,177,220],"alternative":[11,222],"recurrent":[12,63],"neural":[13],"model.":[15],"An":[16],"ESN":[17,50,71,178,218],"consists":[18],"of":[19,22,48,76,86,116,127,160,227,243],"a":[20,35,80,132,158,192],"reservoir":[21],"conventional":[23],"processing":[24],"elements,":[25],"which":[26,38,120],"are":[27],"recurrently":[28],"interconnected":[29],"with":[30,143,150,176],"untrained":[31],"random":[32],"weights,":[33],"and":[34,111,167,207,236],"readout":[36,113],"layer,":[37],"is":[39,51],"trained":[40,100,122],"using":[41,83,101,131],"linear":[42,118],"regression":[43],"methods.":[44],"key":[46],"advantage":[47],"the":[49,52,58,62,74,87,112,128,144,147,151,171,183,214,224,228,241,244],"ability":[53,238],"to":[54,60,72,123,188,200,213,223,239],"model":[55,73,95,149,195,240],"systems":[56],"without":[57],"need":[59],"train":[61,109,184,231],"weights.":[64],"In":[65],"this":[66],"paper,":[67],"we":[68],"use":[69],"production":[75],"speech":[77,103],"signals":[78],"in":[79],"classification":[81,173],"experiment":[82],"isolated":[84,162],"utterances":[85],"English":[88],"digits":[89,163],"\"zero\"":[90],"through":[91],"\"nine.\"":[92],"One":[93],"prediction":[94,155],"for":[96,191],"each":[97],"digit":[98,148],"was":[99,141,179],"frame-based":[102],"features":[104,205],"(cepstral":[105],"coefficients)":[106],"from":[107,146,164],"all":[108],"utterances,":[110],"layer":[114],"consisted":[115],"several":[117],"regressors":[119],"were":[121,211],"target":[124],"different":[125],"portions":[126],"time":[129],"series":[130],"dynamic":[133],"programming":[134],"algorithm":[135],"(Viterbi).":[136],"Each":[137],"novel":[138],"test":[139],"utterance":[140],"classified":[142],"label":[145],"minimum":[152],"mean":[153],"squared":[154],"error.":[156],"Using":[157],"corpus":[159],"4130":[161],"8":[165,168],"male":[166],"female":[169],"speakers,":[170],"highest":[172],"accuracy":[174],"attained":[175],"100.0%":[180,201],"(99.1%)":[181],"on":[182],"(test)":[185],"set,":[186],"compared":[187],"100%":[189],"(94.7%)":[190],"hidden":[193],"Markov":[194],"(HMM).":[196],"HMM":[197,225],"performance":[198],"increased":[199],"(99.8%)":[202],"when":[203],"context":[204],"(first-":[206],"second-order":[208],"temporal":[209],"derivatives)":[210],"appended":[212],"cepstral":[215],"coefficients.":[216],"offers":[219],"attractive":[221],"because":[226],"ESN's":[229],"simple":[230],"procedure,":[232],"low":[233],"computational":[234],"requirements,":[235],"inherent":[237],"dynamics":[242],"signal":[245],"under":[246],"study":[247]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
