{"id":847,"date":"2020-12-08T16:12:25","date_gmt":"2020-12-08T09:12:25","guid":{"rendered":"http:\/\/international.binus.ac.id\/information-system\/?p=847"},"modified":"2020-12-08T16:14:48","modified_gmt":"2020-12-08T09:14:48","slug":"ms4","status":"publish","type":"post","link":"https:\/\/international.binus.ac.id\/information-system\/2020\/12\/08\/ms4\/","title":{"rendered":"Nonlinear multi-model ensemble prediction using dynamic Neural Network with incremental learning"},"content":{"rendered":"<div id=\"\">\n<section class=\"document-abstract document-tab\">\n<div class=\"abstract-desktop-div hide-mobile\">\n<div class=\"abstract-text row\">\n<div class=\"col-12\">\n<div class=\"u-mb-1\">\n<p><strong>Abstract:<\/strong><\/p>\n<div>This paper introduces several nonlinear multi-model ensemble techniques for multiple chaotic models in high-dimensional phase space by means of artificial neural networks. A chaotic model is built by way of the time-delayed phase space reconstruction of the time series from observables. Several predictive global and local models, including Multi-layered Perceptron Neural Network (MLP-NN), are constructed and a number of multi-model ensemble techniques are implemented to produce more accurate hybrid models. One of these techniques is the nonlinear multi-model ensemble using one kind of dynamic neural network so-called Focused Time Delay Neural Network (FTDNN) with batch and incremental learning algorithms. The proposed techniques were used and tested for predicting storm surge dynamics in the North Sea. The results showed that the accuracy of multi-model ensemble predictions is generally improved in comparison to the one by single models. An FTDNN with incremental learning is more desirable for real-time operation, however in our experiments it was less accurate than batch learning.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"u-pb-1 stats-document-abstract-publishedIn\" data-tealium_data=\"{&quot;docType&quot;: &quot;Conference&quot;}\"><strong>Published in: <\/strong><a href=\"https:\/\/ieeexplore.ieee.org\/xpl\/conhome\/6022827\/proceeding\">The 2011 International Joint Conference on Neural Networks<\/a><\/div>\n<div class=\"row u-pt-1\">\n<div class=\"col-6\">\n<div class=\"u-pb-1 doc-abstract-confdate\"><strong>Date of Conference: <\/strong>31 July-5 Aug. 2011<\/div>\n<div class=\"u-pb-1 doc-abstract-dateadded\"><strong>Date Added to IEEE <i>Xplore<\/i>: <\/strong>03 October 2011<\/div>\n<div class=\"u-pb-1\">\n<div><strong><i class=\"icon-caret-abstract\"><\/i>ISBN Information:<\/strong><\/div>\n<\/div>\n<div class=\"u-pb-1\">\n<div role=\"button\"><strong><i class=\"icon-caret-abstract\"><\/i>ISSN Information:<\/strong><\/div>\n<\/div>\n<\/div>\n<div class=\"col-6\">\n<div class=\"u-pb-1\"><strong>INSPEC Accession Number: <\/strong>12287430<\/div>\n<div class=\"u-pb-1 stats-document-abstract-doi\"><strong>DOI: <\/strong><a href=\"https:\/\/doi.org\/10.1109\/IJCNN.2011.6033598\" target=\"_blank\" rel=\"noopener noreferrer\">10.1109\/IJCNN.2011.6033598<\/a><\/div>\n<div class=\"u-pb-1 doc-abstract-publisher\"><span class=\"publisher-info-container black-tooltip\"><span class=\"title\">Publisher: <\/span>IEEE<\/span><\/div>\n<div class=\"u-pb-1 doc-abstract-conferenceLoc\"><strong>Conference Location: <\/strong>San Jose, CA, USA<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n<\/div>\n<section>\n<div id=\"toc-wrapper\" class=\"row full-text-toc-wrapper\">\n<div class=\"row document-full-text-content\">\n<div id=\"full-text-section\" class=\"col col-text stats-document-container-fullTextSection u-printing-display-inline-ie u-printing-display-inline-ff\">\n<p>&nbsp;<\/p>\n<div>\n<div class=\"document-text hide-full-text ng-non-bindable stats-document-dynamicFullTextOrSnippet-container snippet-text show-full-text\">\n<div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: This paper introduces several nonlinear multi-model ensemble techniques for multiple chaotic models in high-dimensional phase space by means of artificial neural networks. A chaotic model is built by way of the time-delayed phase space reconstruction of the time series from observables. Several predictive global and local models, including Multi-layered Perceptron Neural Network (MLP-NN), are [&hellip;]<\/p>\n","protected":false},"author":20,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[],"class_list":["post-847","post","type-post","status-publish","format-standard","hentry","category-achievements"],"_links":{"self":[{"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/posts\/847"}],"collection":[{"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/comments?post=847"}],"version-history":[{"count":2,"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/posts\/847\/revisions"}],"predecessor-version":[{"id":849,"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/posts\/847\/revisions\/849"}],"wp:attachment":[{"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/media?parent=847"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/categories?post=847"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/international.binus.ac.id\/information-system\/wp-json\/wp\/v2\/tags?post=847"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}