{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:48:29Z","timestamp":1774028909244,"version":"3.50.1"},"reference-count":20,"publisher":"Emerald","issue":"10","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"Purpose<\/jats:title>The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.<\/jats:p><\/jats:sec>Findings<\/jats:title>Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.<\/jats:p><\/jats:sec>Research limitations\/implications<\/jats:title>Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.<\/jats:p><\/jats:sec>Practical implications<\/jats:title>MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.<\/jats:p><\/jats:sec>Social implications<\/jats:title>The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.<\/jats:p><\/jats:sec>","DOI":"10.1108\/k-09-2021-0882","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T22:40:56Z","timestamp":1654814456000},"page":"4257-4271","source":"Crossref","is-referenced-by-count":3,"title":["Data-based modeling and identification for general nonlinear dynamical systems by the multidimensional Taylor network"],"prefix":"10.1108","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9305-5427","authenticated-orcid":false,"given":"Hong-Sen","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6584-4891","authenticated-orcid":false,"given":"Zhong-Tian","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Qin","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiao-Jun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3310-9665","authenticated-orcid":false,"given":"Guo-Biao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"key2024091916085481700_ref001","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1109\/ASRU.1997.659011","article-title":"Discriminative model combination","year":"1997"},{"key":"key2024091916085481700_ref002","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105315","article-title":"A reliable time-series method for predicting arthritic disease outcomes: new step from regression toward a nonlinear artificial intelligence method","volume":"189","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"key2024091916085481700_ref003","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.isprsjprs.2018.11.026","article-title":"Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands","volume":"147","year":"2019","journal-title":"Isprs Journal of Photogrammetry and Remote Sensing"},{"issue":"2","key":"key2024091916085481700_ref004","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1039\/c8sc04228d","article-title":"A graph-convolutional neural network model for the prediction of chemical reactivity","volume":"10","year":"2019","journal-title":"Chemical Science"},{"issue":"1","key":"key2024091916085481700_ref005","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.ejor.2007.03.008","article-title":"A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process","volume":"187","year":"2008","journal-title":"European Journal of Operational Research"},{"key":"key2024091916085481700_ref006","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.mfglet.2019.02.001","article-title":"Deep learning for distortion prediction in laser-based additive manufacturing using big data","volume":"20","year":"2019","journal-title":"Manufacturing Letters"},{"issue":"1","key":"key2024091916085481700_ref007","first-page":"1","article-title":"Machine learning-based analysis of sperm videos and participant data for male fertility prediction","volume":"9","year":"2019","journal-title":"Scientific Reports"},{"key":"key2024091916085481700_ref008","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.isatra.2017.12.001","article-title":"Stability analysis and dynamic regulation of multi-dimensional Taylor network controller for SISO nonlinear systems with time-varying delay","volume":"73","year":"2018","journal-title":"ISA Transactions"},{"issue":"5","key":"key2024091916085481700_ref009","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1086\/702846","article-title":"Predicting the thermal and allometric dependencies of disease transmission via the metabolic theory of ecology","volume":"193","year":"2019","journal-title":"American Naturalist"},{"issue":"3","key":"key2024091916085481700_ref010","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1016\/j.jbankfin.2006.02.006","article-title":"Bubbles in the dividend\u2013price ratio? 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