{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T20:07:01Z","timestamp":1760731621654,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031624940"},{"type":"electronic","value":"9783031624957"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-62495-7_9","type":"book-chapter","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:19:24Z","timestamp":1719001164000},"page":"106-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Echo State Networks for\u00a0Modelling of\u00a0Industrial Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0401-3995","authenticated-orcid":false,"given":"Jos\u00e9 Ram\u00f3n","family":"Rodr\u00edguez-Ossorio","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6692-2564","authenticated-orcid":false,"given":"Claudio","family":"Gallicchio","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2762-6949","authenticated-orcid":false,"given":"Antonio","family":"Mor\u00e1n","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0420-2315","authenticated-orcid":false,"given":"Ignacio","family":"D\u00edaz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9023-0341","authenticated-orcid":false,"given":"Juan J.","family":"Fuertes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3921-1599","authenticated-orcid":false,"given":"Manuel","family":"Dom\u00ednguez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.inffus.2018.10.005","volume":"50","author":"A Diez-Olivan","year":"2019","unstructured":"Diez-Olivan, A., Del Ser, J., Galar, D., Sierra, B.: Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0. Inf. Fusion 50, 92\u2013111 (2019). https:\/\/doi.org\/10.1016\/j.inffus.2018.10.005","journal-title":"Inf. Fusion"},{"key":"9_CR2","unstructured":"Dom\u00ednguez, M., Reguera, P., Fuertes, J.J.: Laboratorio remoto para la ense\u00f1anza de la autom\u00e1tica en la universidad de le\u00f3n (espa\u00f1a). Revista Iberoamericana de Autom\u00e1tica e Inform\u00e1tica industrial 2(2), 36\u201345 (2010)"},{"issue":"5","key":"9_CR3","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/J.NEUNET.2011.02.002","volume":"24","author":"C Gallicchio","year":"2011","unstructured":"Gallicchio, C., Micheli, A.: Architectural and Markovian factors of echo state networks. Neural Netw. 24(5), 440\u2013456 (2011). https:\/\/doi.org\/10.1016\/J.NEUNET.2011.02.002","journal-title":"Neural Netw."},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Gallicchio, C., Micheli, A.: Echo state property of deep reservoir computing networks. Cogn. Comput. 9 (2017). https:\/\/doi.org\/10.1007\/s12559-017-9461-9","DOI":"10.1007\/s12559-017-9461-9"},{"key":"9_CR5","unstructured":"Gallicchio, C., Micheli, A.: Deep Echo State Network (DeepESN): A Brief Survey (2020). arXiv:1712.04323 [cs, stat]"},{"key":"9_CR6","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neucom.2016.12.089","volume":"268","author":"C Gallicchio","year":"2017","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: a critical experimental analysis. Neurocomputing 268, 87\u201399 (2017). https:\/\/doi.org\/10.1016\/j.neucom.2016.12.089","journal-title":"Neurocomputing"},{"key":"9_CR7","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/J.NEUNET.2018.08.002","volume":"108","author":"C Gallicchio","year":"2018","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Design of deep echo state networks. Neural Netw. 108, 33\u201347 (2018). https:\/\/doi.org\/10.1016\/J.NEUNET.2018.08.002","journal-title":"Neural Netw."},{"key":"9_CR8","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-319-95098-3_11","volume-title":"Neural Advances in Processing Nonlinear Dynamic Signals","author":"C Gallicchio","year":"2019","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Hierarchical temporal representation in linear reservoir computing. In: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (eds.) WIRN 2017 2017. SIST, vol. 102, pp. 119\u2013129. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-95098-3_11"},{"key":"9_CR9","doi-asserted-by":"publisher","unstructured":"Gao, W., et al.: Deep learning workload scheduling in gpu datacenters: taxonomy, challenges and vision (2022). https:\/\/doi.org\/10.48550\/arXiv.2205.11913, [cs]","DOI":"10.48550\/arXiv.2205.11913"},{"key":"9_CR10","unstructured":"Jaeger, H.: The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note. In: German National Research Center for Information Technology GMD Technical Report, Bonn, Germany, vol. 148, no. 34, p. 13 (2001)"},{"issue":"5667","key":"9_CR11","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1126\/science.1091277","volume":"304","author":"H Jaeger","year":"2004","unstructured":"Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78\u201380 (2004)","journal-title":"Science"},{"issue":"3","key":"9_CR12","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.neunet.2007.04.016","volume":"20","author":"H Jaeger","year":"2007","unstructured":"Jaeger, H., Luko\u0161evi\u0161ius, M., Popovici, D., Siewert, U.: Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw. 20(3), 335\u2013352 (2007). https:\/\/doi.org\/10.1016\/j.neunet.2007.04.016","journal-title":"Neural Netw."},{"key":"9_CR13","doi-asserted-by":"publisher","unstructured":"Johansson, K.H.: The quadruple-tank process: a multivariable laboratory process with an adjustable zero. IEEE Trans. Control Syst. Technol. 8 (2000). https:\/\/doi.org\/10.1109\/87.845876","DOI":"10.1109\/87.845876"},{"key":"9_CR14","doi-asserted-by":"publisher","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015). https:\/\/doi.org\/10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"9_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/978-3-642-35289-8_36","volume-title":"Neural Networks: Tricks of the Trade","author":"M Luko\u0161evi\u010dius","year":"2012","unstructured":"Luko\u0161evi\u010dius, M.: A practical guide to applying echo state networks. In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 659\u2013686. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_36"},{"issue":"3","key":"9_CR16","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","volume":"3","author":"M Luko\u0161evi\u010dius","year":"2009","unstructured":"Luko\u0161evi\u010dius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127\u2013149 (2009)","journal-title":"Comput. Sci. Rev."},{"key":"9_CR17","doi-asserted-by":"publisher","unstructured":"MacGregor, J., Cinar, A.: Monitoring, fault diagnosis, fault-tolerant control and optimization: data driven methods. Comput. Chem. Eng. 47, 111\u2013120 (2012). https:\/\/doi.org\/10.1016\/j.compchemeng.2012.06.017, fOCAPO 2012","DOI":"10.1016\/j.compchemeng.2012.06.017"},{"issue":"3","key":"9_CR18","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1038\/s42256-022-00463-x","volume":"4","author":"M Pandey","year":"2022","unstructured":"Pandey, M., et al.: The transformational role of GPU computing and deep learning in drug discovery. Nat. Mach. Intell. 4(3), 211\u2013221 (2022). https:\/\/doi.org\/10.1038\/s42256-022-00463-x","journal-title":"Nat. Mach. Intell."},{"key":"9_CR19","doi-asserted-by":"publisher","unstructured":"Rodr\u00edguez-Ossorio, J.R., Mor\u00e1n, A., Alonso, S., P\u00e9rez, D., D\u00edaz, I., Dom\u00ednguez, M.: Echo state networks for anomaly detection in industrial systems. IFAC-PapersOnLine 56(2), 1472\u20131477 (2023). https:\/\/doi.org\/10.1016\/j.ifacol.2023.10.1836, 22nd IFAC World Congress","DOI":"10.1016\/j.ifacol.2023.10.1836"},{"key":"9_CR20","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1007\/978-3-031-15934-3_55","volume-title":"ICANN 2022","author":"D Tortorella","year":"2022","unstructured":"Tortorella, D., Gallicchio, C., Micheli, A.: Hierarchical dynamics in deep echo state networks. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds.) ICANN 2022. LNCS, pp. 668\u2013679. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-15934-3_55"},{"key":"9_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1007\/978-3-030-61616-8_40","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2020","author":"N Trouvain","year":"2020","unstructured":"Trouvain, N., Pedrelli, L., Dinh, T.T., Hinaut, X.: ReservoirPy: an efficient and user-friendly library to design echo state networks. In: Farka\u0161, I., Masulli, P., Wermter, S. (eds.) ICANN 2020. LNCS, vol. 12397, pp. 494\u2013505. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61616-8_40"},{"key":"9_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2012.07.005","volume":"35","author":"IB Yildiz","year":"2012","unstructured":"Yildiz, I.B., Jaeger, H., Kiebel, S.J.: Re-visiting the echo state property. Neural Netw. 35, 1\u20139 (2012). https:\/\/doi.org\/10.1016\/j.neunet.2012.07.005","journal-title":"Neural Netw."}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62495-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:20:41Z","timestamp":1719001241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62495-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031624940","9783031624957"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62495-7_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eannconf.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}