{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:34:30Z","timestamp":1765233270906,"version":"3.37.3"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T00:00:00Z","timestamp":1644019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T00:00:00Z","timestamp":1644019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10845-021-01874-0","type":"journal-article","created":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T23:02:38Z","timestamp":1644102158000},"page":"2171-2184","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A novel machine learning algorithm for interval systems approximation based on artificial neural network"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3840-4986","authenticated-orcid":false,"given":"Raouf","family":"Zerrougui","sequence":"first","affiliation":[]},{"given":"Amel B. H.","family":"Adamou-Mitiche","sequence":"additional","affiliation":[]},{"given":"Lahcene","family":"Mitiche","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,5]]},"reference":[{"issue":"11","key":"1874_CR1","doi-asserted-by":"publisher","first-page":"e00938","DOI":"10.1016\/j.heliyon.2018.e00938","volume":"4","author":"OI Abiodun","year":"2018","unstructured":"Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.","journal-title":"Heliyon"},{"key":"1874_CR2","doi-asserted-by":"crossref","unstructured":"Adel, A., & Salah, K. (2016). Model order reduction using artificial neural networks. In 2016 IEEE international conference on electronics, circuits and systems (ICECS) (pp. 89\u201392). IEEE.","DOI":"10.1109\/ICECS.2016.7841139"},{"issue":"9","key":"1874_CR3","doi-asserted-by":"publisher","first-page":"4620","DOI":"10.1016\/j.apm.2011.03.028","volume":"35","author":"OM Alsmadi","year":"2011","unstructured":"Alsmadi, O. M., Abo-Hammour, Z. S., & Al-Smadi, A. M. (2011). Artificial neural network for discrete model order reduction with substructure preservation. Applied Mathematical Modelling, 35(9), 4620\u20134629.","journal-title":"Applied Mathematical Modelling"},{"issue":"2","key":"1874_CR4","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1177\/0142331218762605","volume":"41","author":"RK Gautam","year":"2019","unstructured":"Gautam, R. K., Singh, N., Choudhary, N. K., & Narain, A. (2019). Model order reduction using factor division algorithm and fuzzy c-means clustering technique. Transactions of the Institute of Measurement and Control, 41(2), 468\u2013475.","journal-title":"Transactions of the Institute of Measurement and Control"},{"key":"1874_CR5","unstructured":"Hijazi, Y., Hagen, H., Hansen, C. D., & Joy, K. I. (2008). Why interval arithmetic is so useful. Visualization of large and unstructured data sets."},{"issue":"4","key":"1874_CR6","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1007\/s10845-019-01488-7","volume":"31","author":"Z Huang","year":"2020","unstructured":"Huang, Z., Zhu, J., Lei, J., Li, X., & Tian, F. (2020). Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing, 31(4), 953\u2013966.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"1","key":"1874_CR7","doi-asserted-by":"publisher","first-page":"363","DOI":"10.3182\/20140313-3-IN-3024.00208","volume":"47","author":"A Jaiswal","year":"2014","unstructured":"Jaiswal, A., Singh, P. K., Gangwar, S., Manmatharajan, S., & Kumar, D. (2014). Order reduction of interval systems using Eigen spectrum and factor division algorithm. IFAC Proceedings Volumes, 47(1), 363\u2013367.","journal-title":"IFAC Proceedings Volumes"},{"key":"1874_CR8","doi-asserted-by":"crossref","unstructured":"Kalangadan, A., Priya, N., & Kumar, T. S. (2015). Eigen value computation based approach for reduced order modeling for interval systems. In 2015 Fifth international conference on advances in computing and communications (ICACC) (pp. 137\u2013140). IEEE.","DOI":"10.1109\/ICACC.2015.73"},{"key":"1874_CR9","first-page":"1483","volume":"14","author":"VL Kharitonov","year":"1979","unstructured":"Kharitonov, V. L. (1979). Asymptotic stability of an equilibrium position of a family systems of linear differential equations. Differential Equations, 14, 1483\u20131485.","journal-title":"Differential Equations"},{"issue":"7","key":"1874_CR10","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1007\/s10845-020-01562-5","volume":"31","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Dimitrakopoulos, R., & Maulen, M. (2020). Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex. Journal of Intelligent Manufacturing, 31(7), 1795\u20131811.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1874_CR11","doi-asserted-by":"crossref","unstructured":"Mohamed, K. S. (2018). Machine learning for model order reduction. Springer.","DOI":"10.1007\/978-3-319-75714-8"},{"key":"1874_CR12","unstructured":"Moore, R. E. (1966). Interval analysis. Prentice-Hall."},{"key":"1874_CR13","doi-asserted-by":"crossref","unstructured":"Moore, R. E., Kearfott, R. B., & Cloud, M. J. (2009). Introduction to interval analysis. Society for Industrial and Applied Mathematics.","DOI":"10.1137\/1.9780898717716"},{"key":"1874_CR14","doi-asserted-by":"crossref","unstructured":"Mor\u00e9, J. J. (1978). The Levenberg\u2013Marquardt algorithm: Implementation and theory. In Numerical analysis (pp. 105\u2013116). Springer.","DOI":"10.1007\/BFb0067700"},{"issue":"12","key":"1874_CR15","first-page":"12721","volume":"119","author":"AP Padhy","year":"2018","unstructured":"Padhy, A. P., Singh, V. P., & Pattnaik, S. (2018). Model reduction of multi-input\u2013multi-output discrete interval systems using gain adjustment. International Journal of Pure and Applied Mathematics, 119(12), 12721\u201312739.","journal-title":"International Journal of Pure and Applied Mathematics"},{"key":"1874_CR16","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.mejo.2017.04.007","volume":"65","author":"K Salah","year":"2017","unstructured":"Salah, K. (2017). A novel model order reduction technique based on artificial intelligence. Microelectronics Journal, 65, 58\u201371.","journal-title":"Microelectronics Journal"},{"issue":"6","key":"1874_CR17","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1007\/s10444-018-9590-z","volume":"44","author":"O San","year":"2018","unstructured":"San, O., & Maulik, R. (2018). Neural network closures for nonlinear model order reduction. Advances in Computational Mathematics, 44(6), 1717\u20131750.","journal-title":"Advances in Computational Mathematics"},{"key":"1874_CR18","doi-asserted-by":"crossref","unstructured":"Schilders, W. H., Van der Vorst, H. A., & Rommes, J. (2008). Model order reduction: Theory, research aspects and applications (Vol. 13, p. 13). Springer.","DOI":"10.1007\/978-3-540-78841-6"},{"key":"1874_CR19","doi-asserted-by":"crossref","unstructured":"Selvaganesan, N. (2007). Mixed method of model reduction for uncertain systems. Serbian Journal of Electrical Engineering, 4(1), 1\u201312.","DOI":"10.2298\/SJEE0701001S"},{"key":"1874_CR20","doi-asserted-by":"crossref","unstructured":"Sharma, M. K., & Kumar, D. (2015). Modified $$\\gamma ^{-\\delta }$$ Routh approximation method for order reduction of discrete interval systems. In 2015 10th Asian control conference (ASCC) (pp. 1\u20135). IEEE.","DOI":"10.1109\/ASCC.2015.7244881"},{"key":"1874_CR21","doi-asserted-by":"crossref","unstructured":"Singh, V. P., & Chandra, D. (2011). Model reduction of discrete interval system using dominant poles retention and direct series expansion method. In 2011 5th International power engineering and optimization conference (pp. 27\u201330). IEEE.","DOI":"10.1109\/PEOCO.2011.5970421"},{"key":"1874_CR22","first-page":"1","volume":"22","author":"H Tercan","year":"2021","unstructured":"Tercan, H., Deibert, P., & Meisen, T. (2021). Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer. Journal of Intelligent Manufacturing, 22, 1\u201310.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"7","key":"1874_CR23","first-page":"879","volume":"232","author":"N Vijaya Anand","year":"2018","unstructured":"Vijaya Anand, N., Siva Kumar, M., & Srinivasa Rao, R. (2018). A novel reduced order modeling of interval system using soft computing optimization approach. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 232(7), 879\u2013894.","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering"},{"issue":"5","key":"1874_CR24","doi-asserted-by":"publisher","first-page":"326","DOI":"10.4103\/0377-2063.48531","volume":"54","author":"CB Vishwakarma","year":"2008","unstructured":"Vishwakarma, C. B., & Prasad, R. (2008). Clustering method for reducing order of linear system using Pade approximation. IETE Journal of Research, 54(5), 326\u2013330.","journal-title":"IETE Journal of Research"},{"key":"1874_CR25","doi-asserted-by":"crossref","unstructured":"Waszczyszyn, Z. (Ed.). (1999). Neural networks in the analysis and design of structures (p. 307). Springer.","DOI":"10.1007\/978-3-7091-2484-0"},{"key":"1874_CR26","doi-asserted-by":"crossref","unstructured":"Zerrougui, R., Adamou-Mitiche, A. B. H., & Mitiche, L. (2021). Projection approach for interval systems approximation: An extension to MIMO systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 6, 66.","DOI":"10.1109\/TCSII.2021.3091056"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01874-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-021-01874-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01874-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T12:09:04Z","timestamp":1682510944000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-021-01874-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,5]]},"references-count":26,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["1874"],"URL":"https:\/\/doi.org\/10.1007\/s10845-021-01874-0","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"type":"print","value":"0956-5515"},{"type":"electronic","value":"1572-8145"}],"subject":[],"published":{"date-parts":[[2022,2,5]]},"assertion":[{"value":"18 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}