Publicação
NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY
| Resumo: | Abstract: Geocentric translation model (GTM) in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP) and new parameters determined using the arithmetic mean (AM) were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM) attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m. |
|---|---|
| Autores principais: | Ziggah,Yao Yevenyo |
| Outros Autores: | Youjian,Hu; Laari,Prosper Basommi; Hui,Zhenyang |
| Assunto: | Geocentric translation model Backpropagation neural network Radial basis function neural network Generalized regression neural network Coordinate transformation |
| Ano: | 2017 |
| País: | Brasil |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | IBICT |
| Idioma: | inglês |
| Origem: | Oasisbr |
| _version_ | 1871505397896970240 |
|---|---|
| author | Ziggah,Yao Yevenyo |
| author2 | Youjian,Hu Laari,Prosper Basommi Hui,Zhenyang |
| author2_role | author author author |
| author_facet | Ziggah,Yao Yevenyo Youjian,Hu Laari,Prosper Basommi Hui,Zhenyang |
| author_role | author |
| country_str | BR |
| creators_json_txt | [{\"Person.name\":\"Ziggah,Yao Yevenyo\"},{\"Person.name\":\"Youjian,Hu\"},{\"Person.name\":\"Laari,Prosper Basommi\"},{\"Person.name\":\"Hui,Zhenyang\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Ziggah,Yao Yevenyo Youjian,Hu Laari,Prosper Basommi Hui,Zhenyang |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Geocentric translation model Backpropagation neural network Radial basis function neural network Generalized regression neural network Coordinate transformation |
| datacite.titles.title.fl_str_mv | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY |
| dc.creator.none.fl_str_mv | Ziggah,Yao Yevenyo Youjian,Hu Laari,Prosper Basommi Hui,Zhenyang |
| dc.format.none.fl_str_mv | text/html |
| dc.identifier.none.fl_str_mv | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1982-21702017000100213 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Universidade Federal do Paraná |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.source.none.fl_str_mv | Boletim de Ciências Geodésicas v.23 n.1 2017 reponame:Boletim de Ciências Geodésicas instname:Universidade Federal do Paraná (UFPR) instacron:UFPR |
| dc.subject.none.fl_str_mv | Geocentric translation model Backpropagation neural network Radial basis function neural network Generalized regression neural network Coordinate transformation |
| dc.title.fl_str_mv | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Abstract: Geocentric translation model (GTM) in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP) and new parameters determined using the arithmetic mean (AM) were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM) attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| id | oasisbr_f3f5927fe654870dbcfbde5aaa6d0da8 |
| identifier.url.fl_str_mv | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1982-21702017000100213 |
| inst_facet_str | urn:organizationAcronym:oasisbr{{{_:::_}}}IBICT |
| instacron_str | OASISBR |
| institution | IBICT |
| instname_str | IBICT |
| language | eng |
| network_acronym_str | oasisbr |
| network_name_str | Oasisbr |
| oai_identifier_str | oai:scielo:S1982-21702017000100213 |
| organization_str_mv | urn:organizationAcronym:oasisbr |
| person_str_mv | Ziggah,Yao Yevenyo Youjian,Hu Laari,Prosper Basommi Hui,Zhenyang |
| publishDate | 2017 |
| publisher.none.fl_str_mv | Universidade Federal do Paraná |
| repo_facet_str | urn:repositoryAcronym:oasisbr{{{_:::_}}}Oasisbr |
| reponame_str | Oasisbr |
| repository_id_str | urn:repositoryAcronym:oasisbr |
| service_str_mv | urn:repositoryAcronym:oasisbr |
| spelling | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGYZiggah,Yao YevenyoYoujian,HuLaari,Prosper BasommiHui,ZhenyangGeocentric translation modelBackpropagation neural networkRadial basis function neural networkGeneralized regression neural networkCoordinate transformationopen accesshttp://purl.org/coar/access_right/c_abf2http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1982-21702017000100213URLhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1982-21702017000100213URLHasVersion2017-03-01Abstract: Geocentric translation model (GTM) in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP) and new parameters determined using the arithmetic mean (AM) were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM) attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.engtext/htmlUniversidade Federal do ParanáBoletim de Ciências Geodésicas v.23 n.1 2017reponame:Boletim de Ciências Geodésicasinstname:Universidade Federal do Paraná (UFPR)instacron:UFPRjournal articlehttp://purl.org/coar/resource_type/c_6501literatureVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85 |
| spellingShingle | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY Ziggah,Yao Yevenyo Geocentric translation model Backpropagation neural network Radial basis function neural network Generalized regression neural network Coordinate transformation |
| status | SINGLETON |
| status_str | VoR |
| subject.fl_str_mv | Geocentric translation model Backpropagation neural network Radial basis function neural network Generalized regression neural network Coordinate transformation |
| title | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY |
| title_full | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY |
| title_fullStr | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY |
| title_full_unstemmed | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY |
| title_short | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY |
| title_sort | NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY |
| topic | Geocentric translation model Backpropagation neural network Radial basis function neural network Generalized regression neural network Coordinate transformation |
| topic_facet | Geocentric translation model Backpropagation neural network Radial basis function neural network Generalized regression neural network Coordinate transformation |
| url | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1982-21702017000100213 |
| visible | 1 |
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