{"created":"2023-05-15T12:30:54.625207+00:00","id":483,"links":{},"metadata":{"_buckets":{"deposit":"bdfa9391-b202-4267-9dcf-9982e9590cb3"},"_deposit":{"created_by":2,"id":"483","owners":[2],"pid":{"revision_id":0,"type":"depid","value":"483"},"status":"published"},"_oai":{"id":"oai:fit.repo.nii.ac.jp:00000483","sets":["256:305:306"]},"author_link":["2312","2317","2318","2313","2314","2315","2316","2310","2311"],"item_3_alternative_title_23":{"attribute_name":"タイトル（ヨミ）","attribute_value_mlt":[{"subitem_alternative_title":"Echo state network ニヨル ジケイレツ カラノ ヒテイジョウセイ ノ ケンシュツ"}]},"item_3_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2018-12","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"46","bibliographicPageStart":"43","bibliographicVolumeNumber":"1","bibliographic_titles":[{"bibliographic_title":"福岡工業大学総合研究機構研究所所報"}]}]},"item_3_description_17":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_3_description_47":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"subitem_description":"論文(Article)","subitem_description_type":"Other"}]},"item_3_description_5":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"We developed a novel method that detecting nonstationarity in observed time-series. Using reservoir computing approach, which now becomes a popular tool in machine learning field, we estimated cross-prediction errors between different segments of time-series. These errors are considered as a measure of non-similarities between different segments. 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