{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:23Z","timestamp":1740122783846,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"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":["Neural Process Lett"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11063-022-10997-1","type":"journal-article","created":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T03:36:12Z","timestamp":1661398572000},"page":"3045-3079","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Long Short Term Memory Based Self Tuning Regulator Design for Nonlinear Systems"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6816-1180","authenticated-orcid":false,"given":"\u00c7a\u011fatay","family":"Sanatel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00fclay \u00d6ke","family":"G\u00fcnel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"10997_CR1","unstructured":"Bob\u00e1l V, B\u00f6hm J, Fessl J, Mach\u00e1cek J (2005) Digital self-tunning controllers. Advanced textbooks in control and signal processing. Springer-Verlag, London, pp 5\u2013137"},{"key":"10997_CR2","doi-asserted-by":"crossref","unstructured":"Landau ID, Lozano R, M\u2019Saad M, Karimi A (2011) Adaptive control. Algorithm, analysis and applications, 2nd edn. Springer, London, pp 193\u2013407","DOI":"10.1007\/978-0-85729-664-1_11"},{"issue":"S1","key":"10997_CR3","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/s00521-016-2387-4","volume":"28","author":"U Kemal","year":"2017","unstructured":"Kemal U, G\u00fclay \u00d6G (2017) Generalized self-tuning regulator based on online support vector regression. Neural Comput Appl 28(S1):775\u2013801. https:\/\/doi.org\/10.1007\/s00521-016-2387-4","journal-title":"Neural Comput Appl"},{"issue":"5","key":"10997_CR4","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/0005-1098(83)90002-X","volume":"19","author":"KS Ast\u00f6m","year":"1983","unstructured":"Ast\u00f6m KS (1983) Theory and applications of adaptive control. Automatica (Journal of IFAC) 19(5):471\u2013486. https:\/\/doi.org\/10.1016\/0005-1098(83)90002-X","journal-title":"Automatica (Journal of IFAC)"},{"key":"10997_CR5","unstructured":"Stoica P, S\u00f6derstr\u00f6m T (1989) System identification. Prentice hall international series in systems and control engineering. Prentice Hall, Michigan"},{"issue":"8","key":"10997_CR6","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"10997_CR7","unstructured":"Zhou C, Sun C, Liu Z, Lau F (2005) A C-LSTM neural network for text classification. Cornell University, pp 1\u201310 arXiv:1511.08630"},{"key":"10997_CR8","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","volume":"337","author":"G Liu","year":"2019","unstructured":"Liu G, Guo J (2019) Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Elsever Neurocomputing 337:325\u2013338. https:\/\/doi.org\/10.1016\/j.neucom.2019.01.078","journal-title":"Elsever Neurocomputing"},{"key":"10997_CR9","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/978-3-319-50496-4_20","volume":"10102","author":"C Dong","year":"2016","unstructured":"Dong C, Zhang J, Zong C, Hattori M, Di H (2016) Character-based LSTM-CRF with radical-level features for chinese named entity recognition. Nat Lang Underst and Intelligent Appl, LNAI 10102:239\u2013250. https:\/\/doi.org\/10.1007\/978-3-319-50496-4_20","journal-title":"Nat Lang Underst and Intelligent Appl, LNAI"},{"key":"10997_CR10","unstructured":"Malhotra P, Ramakrishnan A, Anand G, Vig L, Agarwal P, Shroff G (2016) LSTM-based encoder-decoder for multi-sensor anomaly detection. Cornell University, pp 1\u20135 arXiv:1607.00148"},{"key":"10997_CR11","doi-asserted-by":"publisher","unstructured":"Nicola F, Fujimoto Y, Oboe R (2018) A LSTM Neural network applied to mobile robots path planning, IEEE, Porto, pp 1\u20136, https:\/\/doi.org\/10.1109\/INDIN.2018.8472028","DOI":"10.1109\/INDIN.2018.8472028"},{"key":"10997_CR12","doi-asserted-by":"publisher","unstructured":"Ruslan F, Samad A, Zain Z, Adnan R (2013) Flood prediction using NARX neural network and EKF prediction technique: a comparative study. In: IEEE 3rd international conference on system engineering and technology, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICSEngT.2013.6650171","DOI":"10.1109\/ICSEngT.2013.6650171"},{"key":"10997_CR13","unstructured":"KERAS library web site: (2021) https:\/\/keras.io\/"},{"key":"10997_CR14","doi-asserted-by":"crossref","unstructured":"Graves A (2012) Supervised sequence labelling with recurrent neural networks. Springer, Berlin, Heidelberg, pp 37\u201345","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"10997_CR15","unstructured":"Analytics Vidhya web site: Pranjal Srivastava (2017) Essentials of deep learning : introduction to long short term memory, https:\/\/www.analyticsvidhya.com\/blog\/2017\/12\/fundamentals-of-deep-learning-introduction-to-lstm\/"},{"key":"10997_CR16","unstructured":"Colah github web site: Felix Gers, Fred Cummins, Santiago Fernandez, Justin Bayer, Daan Wierstra, Julian Togelius, Faustino Gomez, Matteo Gagliolo, Alex Graves (2015) Understanding LSTM Networks: http:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/"},{"key":"10997_CR17","first-page":"163","volume":"2","author":"W Duch","year":"1999","unstructured":"Duch W, Jankowski N (1999) Survey of neural transfer functions. Neural Comput Surv 2:163\u2013213","journal-title":"Neural Comput Surv"},{"key":"10997_CR18","unstructured":"Sak H, Senior A, Beaufays F (2018) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Cornell University, pp 1\u20135 arXiv:1402.1128"},{"issue":"12","key":"10997_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5281\/zenodo.1339748","volume":"10","author":"D L\u00f3pez","year":"2016","unstructured":"L\u00f3pez D, Verai N, Pedraza L (2016) Analysis of multilayer neural network modeling and long short-term memory. World Acad Sci Eng Technol Int J Math Comp Sci 10(12):1\u20136. https:\/\/doi.org\/10.5281\/zenodo.1339748","journal-title":"World Acad Sci Eng Technol Int J Math Comp Sci"},{"key":"10997_CR20","unstructured":"Towards data science web site: Jae Duk Seo, Aidan Gomez (2018) [Back to Basics] Deriving Back Propagation on simple RNN\/LSTM (feat. Aidan Gomez), https:\/\/towardsdatascience.com\/back-to-basics-deriving-back-propagation-on-simple-rnn-lstm-feat-aidan-gomez-c7f286ba973d\/"},{"key":"10997_CR21","unstructured":"Golnaraghi F, Kuo B (2017) Automatic control systems, 9th edn. McGraw-Hill Education, United States of America, pp 2\u2013223"},{"key":"10997_CR22","doi-asserted-by":"publisher","unstructured":"Akhyar S, Omatu S (1993) Self-tuning PID control by neural networks. In: Proceedings of 1993 international joint conference on neural networks, IEEE, pp 2749\u20132752, https:\/\/doi.org\/10.1109\/IJCNN.1993.714292","DOI":"10.1109\/IJCNN.1993.714292"},{"key":"10997_CR23","doi-asserted-by":"crossref","unstructured":"Sung SW, Lee J, Lee IB (2009) Process identification and PID control. IEEE Press, Wiley, Singapore, pp 275\u2013343","DOI":"10.1002\/9780470824122"},{"issue":"13","key":"10997_CR24","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1002\/rnc.1524","volume":"20","author":"S \u0130plik\u00e7i","year":"2010","unstructured":"\u0130plik\u00e7i S (2010) A comparative study on a novel model-based PID tuning and control mechanism for nonlinear systems. Int J Robust Nonlinear Control 20(13):1483\u20131501. https:\/\/doi.org\/10.1002\/rnc.1524","journal-title":"Int J Robust Nonlinear Control"},{"issue":"6","key":"10997_CR25","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.1007\/s00500-019-04223-9","volume":"24","author":"K U\u00e7ak","year":"2019","unstructured":"U\u00e7ak K, G\u00fcnel G\u00d6 (2019) An adaptive sliding mode controller based on online support vector regression for nonlinear systems. Soft Comput 24(6):4623\u20134643. https:\/\/doi.org\/10.1007\/s00500-019-04223-9","journal-title":"Soft Comput"},{"key":"10997_CR26","doi-asserted-by":"publisher","unstructured":"Borisson U, Wittenmark B (1974) An industrial application of a self tuning regulator. In: 4th IFAC\/IFIP international conference on digital computer applications to process control. Lecture notes in economics and mathematical systems, Springer, Berlin, Heidelberg, Vol 93. pp 76\u201387, https:\/\/doi.org\/10.1007\/978-3-642-65796-2_7","DOI":"10.1007\/978-3-642-65796-2_7"},{"issue":"5","key":"10997_CR27","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/0005-1098(77)90067-X","volume":"13","author":"KS Ast\u00f6m","year":"1977","unstructured":"Ast\u00f6m KS, Borisson U, Ljung L, Wittenmark B (1977) Theory and applications of self-tuning regulators. Elsevier, Automatica 13(5):457\u2013476. https:\/\/doi.org\/10.1016\/0005-1098(77)90067-X","journal-title":"Elsevier, Automatica"},{"issue":"11","key":"10997_CR28","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1002\/rnc.727","volume":"12","author":"MT Hagan","year":"2002","unstructured":"Hagan MT, Demuth HB, De Jesus O (2002) An introduction to the use of neural networks in control systems. Int J Robust Nonlinear Control 12(11):959\u2013985. https:\/\/doi.org\/10.1002\/rnc.727","journal-title":"Int J Robust Nonlinear Control"},{"key":"10997_CR29","unstructured":"Sanatel \u00c7, G\u00fcnel G\u00d6 (2020) Long short term memory based system identificationand adaptive control, Thesis (M.Sc.), \u0130stanbul Technical University, Institute of Science and Technology, pp 63\u201378"},{"issue":"4","key":"10997_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S0218126618500652","volume":"27","author":"A Zribi","year":"2015","unstructured":"Zribi A, Chtourou M, Djemel M (2015) A new PID neural network controller design for nonlinear processes. J Circuits Syst Comput 27(4):1\u201311. https:\/\/doi.org\/10.1142\/S0218126618500652","journal-title":"J Circuits Syst Comput"},{"key":"10997_CR31","unstructured":"Staudemeyer RC, Morris ER (2019) Understanding LSTM a tutorial into long short-term memory recurrent neural networks. Cornell University, pp 1\u201342 arXiv:1909.09586"},{"key":"10997_CR32","unstructured":"Alpayd\u0131n E (2004) Introduction to machine learning, The MIT Press Essential Knowledge series, pp 197\u2013271"},{"key":"10997_CR33","doi-asserted-by":"publisher","unstructured":"Shang W, Shengdun Z, Yajing S (2008) Adaptive PID controller based on online LSSVM identification. IEEE\/ASME international conference on advanced intelligent mechatronics. pp 1\u20135, https:\/\/doi.org\/10.1109\/AIM.2008.4601744","DOI":"10.1109\/AIM.2008.4601744"},{"issue":"7","key":"10997_CR34","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1002\/aic.690340708","volume":"34","author":"C Kravaris","year":"1988","unstructured":"Kravaris C, Palanki S (1988) Robust nonlinear state feedback under structured uncertainty. AIChE J 34(7):1119\u20131127","journal-title":"AIChE J"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10997-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-10997-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10997-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T12:13:22Z","timestamp":1688818402000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-10997-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,25]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["10997"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-10997-1","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2022,8,25]]},"assertion":[{"value":"8 August 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}