{"id":"https://openalex.org/W1997889862","doi":"https://doi.org/10.4018/jamc.2012070104","title":"Comparing LR, GP, BPN, RBF and SVR for Self-Learning Pattern Matching in WSN Indoor Localization","display_name":"Comparing LR, GP, BPN, RBF and SVR for Self-Learning Pattern Matching in WSN Indoor Localization","publication_year":2012,"publication_date":"2012-07-01","ids":{"openalex":"https://openalex.org/W1997889862","doi":"https://doi.org/10.4018/jamc.2012070104","mag":"1997889862"},"language":"en","primary_location":{"id":"doi:10.4018/jamc.2012070104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/jamc.2012070104","pdf_url":null,"source":{"id":"https://openalex.org/S7509020","display_name":"International Journal of Applied Metaheuristic Computing","issn_l":"1947-8283","issn":["1947-8283","1947-8291"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Applied Metaheuristic Computing","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/A5043595446","display_name":"Ray-I Chang","orcid":"https://orcid.org/0000-0002-8737-7227"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Ray-I Chang","raw_affiliation_strings":["National Taiwan University, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000796225","display_name":"Chi\u2010Cheng Chuang","orcid":"https://orcid.org/0000-0003-1898-7264"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chi-Cheng Chuang","raw_affiliation_strings":["National Taiwan University, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043595446"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07016249,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":"3","first_page":"49","last_page":"62"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/computer-science","display_name":"Computer science","score":0.7730580568313599},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.7428885102272034},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6112083792686462},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.542255699634552},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5158304572105408},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4883884787559509},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4756488502025604},{"id":"https://openalex.org/keywords/radial-basis-function","display_name":"Radial basis function","score":0.474933922290802},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.4686547815799713},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43944051861763},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.42481088638305664},{"id":"https://openalex.org/keywords/multipath-propagation","display_name":"Multipath propagation","score":0.4151623249053955},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3505006432533264},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3483177423477173},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12072432041168213},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07475888729095459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7730580568313599},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7428885102272034},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6112083792686462},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.542255699634552},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5158304572105408},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4883884787559509},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4756488502025604},{"id":"https://openalex.org/C98856871","wikidata":"https://www.wikidata.org/wiki/Q1588488","display_name":"Radial basis function","level":3,"score":0.474933922290802},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.4686547815799713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43944051861763},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.42481088638305664},{"id":"https://openalex.org/C161218011","wikidata":"https://www.wikidata.org/wiki/Q11827794","display_name":"Multipath propagation","level":3,"score":0.4151623249053955},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3505006432533264},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3483177423477173},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12072432041168213},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07475888729095459},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.4018/jamc.2012070104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/jamc.2012070104","pdf_url":null,"source":{"id":"https://openalex.org/S7509020","display_name":"International Journal of Applied Metaheuristic Computing","issn_l":"1947-8283","issn":["1947-8283","1947-8291"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Applied Metaheuristic Computing","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:igg:jamc00:v:3:y:2012:i:3:p:49-62","is_oa":false,"landing_page_url":"https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jamc.2012070104","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W124077135","https://openalex.org/W1570448133","https://openalex.org/W1979638981","https://openalex.org/W1987587623","https://openalex.org/W2039943351","https://openalex.org/W2068326278","https://openalex.org/W2073772406","https://openalex.org/W2098663568","https://openalex.org/W2108424774","https://openalex.org/W2119362555","https://openalex.org/W2121553646","https://openalex.org/W2122250590","https://openalex.org/W2125254044","https://openalex.org/W2125383843","https://openalex.org/W2132872612","https://openalex.org/W2141811947","https://openalex.org/W2170102584","https://openalex.org/W2537952967","https://openalex.org/W2966207845"],"related_works":["https://openalex.org/W4386939572","https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W4300066510","https://openalex.org/W2056958800","https://openalex.org/W2803685231","https://openalex.org/W4293503520","https://openalex.org/W3134152097","https://openalex.org/W4311388919","https://openalex.org/W2966696655"],"abstract_inverted_index":{"It":[0],"is":[1,138,171],"a":[2,66,82,130],"challenging":[3],"issue":[4],"to":[5,11,36,50,70,86,97,121,151,158,177],"apply":[6],"WSN":[7],"(Wireless":[8],"Sensor":[9],"Network)":[10],"achieve":[12,159,175],"accurate":[13],"location":[14],"information.":[15],"PM":[16,128,141,157],"(Pattern":[17],"Matching),":[18],"known":[19],"as":[20,96],"one":[21],"of":[22,31,62,74,125,168],"the":[23,29,44,54,59,72,88,123,160,165],"most":[24],"famous":[25],"localization":[26,55,99,162,180],"methods,":[27],"has":[28],"drawback":[30],"requiring":[32],"high":[33],"initialization":[34,60],"effort":[35,61],"predict/train":[37],"MF":[38],"(Matching":[39],"Function).":[40],"In":[41],"this":[42],"paper,":[43],"authors":[45],"propose":[46],"SPM":[47,64,126,137,147,173],"(Self-learning":[48],"PM)":[49],"improve":[51],"not":[52],"only":[53],"accuracy":[56,100,181],"but":[57],"also":[58],"PM.":[63,183],"applies":[65,104],"divide-and-conquer":[67],"self-learning":[68],"scheme":[69,85],"reduce":[71],"number":[73,167],"training":[75,106,144,154,169],"patterns":[76,155,170],"in":[77,129],"training.":[78],"Additionally,":[79],"it":[80],"introduces":[81],"Bayesian":[83],"filtering":[84],"remove":[87],"noise":[89],"signal":[90],"caused":[91],"by":[92],"multipath":[93],"effects":[94],"so":[95],"enhance":[98],"accordingly.":[101],"This":[102],"paper":[103],"different":[105],"methods":[107,145],"(linear":[108],"regression,":[109],"Gaussian":[110],"process,":[111],"backpropagation":[112],"network,":[113],"radial":[114],"basis":[115],"function,":[116],"and":[117,127],"support":[118],"vector":[119],"regression)":[120],"evaluate":[122],"performances":[124],"complicated":[131],"indoor":[132],"environment.":[133],"Experiments":[134],"show":[135],"that":[136],"better":[139],"than":[140,156,182],"for":[142],"all":[143],"applied.":[146],"can":[148,174],"use":[149],"up":[150,176],"72%":[152],"fewer":[153],"same":[161,166],"accuracy.":[163],"If":[164],"utilized,":[172],"58%":[178],"higher":[179]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
