{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1361137046152948992.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1088/0305-4470/39/34/009"}},{"identifier":{"@type":"URI","@value":"http://stacks.iop.org/0305-4470/39/i=34/a=009/pdf"}},{"identifier":{"@type":"DOI","@value":"10.48550/arxiv.nlin/0507006"}}],"dc:title":[{"@value":"Detecting generalized synchronization between chaotic signals: a kernel-based approach"}],"description":[{"notation":[{"@value":"A unified framework for analyzing generalized synchronization in coupled chaotic systems from data is proposed. The key of the proposed approach is the use of the kernel methods recently developed in the field of machine learning. Several successful applications are presented, which show the capability of the kernel-based approach for detecting generalized synchronization. It is also shown that the dynamical change of the coupling coefficient between two chaotic systems can be captured by the proposed approach."}]},{"notation":[{"@value":"20 pages, 15 figures. massively revised as a full paper; issues on the choice of parameters by cross validation, tests by surrogated data, etc. are added as well as additional examples and figures"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1381137046152948994","@type":"Researcher","foaf:name":[{"@value":"Hiromichi Suetani"}]},{"@id":"https://cir.nii.ac.jp/crid/1381137046152948993","@type":"Researcher","foaf:name":[{"@value":"Yukito Iba"}]},{"@id":"https://cir.nii.ac.jp/crid/1381137046152948992","@type":"Researcher","foaf:name":[{"@value":"Kazuyuki Aihara"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"03054470"},{"@type":"EISSN","@value":"13616447"}],"prism:publicationName":[{"@value":"Journal of Physics A: Mathematical and General"}],"dc:publisher":[{"@value":"IOP Publishing"}],"prism:publicationDate":"2006-08-09","prism:volume":"39","prism:number":"34","prism:startingPage":"10723","prism:endingPage":"10742"},"reviewed":"false","dcterms:accessRights":"http://purl.org/coar/access_right/c_abf2","url":[{"@id":"http://stacks.iop.org/0305-4470/39/i=34/a=009/pdf"}],"createdAt":"2006-08-10","modifiedAt":"2020-04-11","foaf:topic":[{"@id":"https://cir.nii.ac.jp/all?q=Statistical%20Mechanics%20(cond-mat.stat-mech)","dc:title":"Statistical Mechanics (cond-mat.stat-mech)"},{"@id":"https://cir.nii.ac.jp/all?q=Physics%20-%20Data%20Analysis,%20Statistics%20and%20Probability","dc:title":"Physics - Data Analysis, Statistics and Probability"},{"@id":"https://cir.nii.ac.jp/all?q=FOS:%20Physical%20sciences","dc:title":"FOS: Physical sciences"},{"@id":"https://cir.nii.ac.jp/all?q=Chaotic%20Dynamics%20(nlin.CD)","dc:title":"Chaotic Dynamics (nlin.CD)"},{"@id":"https://cir.nii.ac.jp/all?q=Nonlinear%20Sciences%20-%20Chaotic%20Dynamics","dc:title":"Nonlinear Sciences - Chaotic Dynamics"},{"@id":"https://cir.nii.ac.jp/all?q=Condensed%20Matter%20-%20Statistical%20Mechanics","dc:title":"Condensed Matter - Statistical Mechanics"},{"@id":"https://cir.nii.ac.jp/all?q=Data%20Analysis,%20Statistics%20and%20Probability%20(physics.data-an)","dc:title":"Data Analysis, Statistics and Probability (physics.data-an)"}],"relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360285705652126336","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"A RANSAC-Based ISOMAP for Filiform Manifolds in Nonlinear Dynamical Systems –An Application to Chaos in a Dripping Faucet–"}]},{"@id":"https://cir.nii.ac.jp/crid/1360285711141842688","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Higher-Order Regularized Kernel Canonical Correlation Analysis"}]},{"@id":"https://cir.nii.ac.jp/crid/1360588380145887616","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Enhancing spectral analysis in nonlinear dynamics with pseudoeigenfunctions from continuous spectra"}]},{"@id":"https://cir.nii.ac.jp/crid/1390001205346002816","@type":"Article","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Detecting generalized synchronization"}]},{"@id":"https://cir.nii.ac.jp/crid/1390282679444583680","@type":"Article","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"ja","@value":"正準相関分析入門"},{"@language":"en","@value":"Introduction to Canonical Correlation Analysis"},{"@value":"Introduction to canonical correlation analysis: Mutual information extraction from multimodal observations"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1088/0305-4470/39/34/009"},{"@type":"OPENAIRE","@value":"doi_dedup___::c5a526f0c54dce3cafb213bd5261cec0"},{"@type":"CROSSREF","@value":"10.3902/jnns.20.62_references_DOI_8YvbVZklJZMRbFX90Pzbszrvp5R"},{"@type":"CROSSREF","@value":"10.1007/978-3-642-21738-8_36_references_DOI_8YvbVZklJZMRbFX90Pzbszrvp5R"},{"@type":"CROSSREF","@value":"10.1142/s0218001415510052_references_DOI_8YvbVZklJZMRbFX90Pzbszrvp5R"},{"@type":"CROSSREF","@value":"10.1587/nolta.3.113_references_DOI_8YvbVZklJZMRbFX90Pzbszrvp5R"},{"@type":"CROSSREF","@value":"10.1038/s41598-024-69837-y_references_DOI_8YvbVZklJZMRbFX90Pzbszrvp5R"}]}