{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T20:37:30Z","timestamp":1760647050811,"version":"3.37.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030264734"},{"type":"electronic","value":"9783030264741"}],"license":[{"start":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T00:00:00Z","timestamp":1563926400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-26474-1_36","type":"book-chapter","created":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T04:02:44Z","timestamp":1563854564000},"page":"513-531","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Hybrid Methods of GMDH-Neural Networks Synthesis and Training for Solving Problems of Time Series Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1536-5542","authenticated-orcid":false,"given":"Volodymyr","family":"Lytvynenko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0843-8053","authenticated-orcid":false,"given":"Waldemar","family":"Wojcik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1140-0985","authenticated-orcid":false,"given":"Andrey","family":"Fefelov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8915-728X","authenticated-orcid":false,"given":"Iryna","family":"Lurie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8339-1219","authenticated-orcid":false,"given":"Nataliia","family":"Savina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5392-5125","authenticated-orcid":false,"given":"Mariia","family":"Voronenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7391-0986","authenticated-orcid":false,"given":"Oleg","family":"Boskin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8411-3584","authenticated-orcid":false,"given":"Saule","family":"Smailova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,7,24]]},"reference":[{"key":"36_CR1","unstructured":"Ivakhnenko AG (1971) Heuristic self-organising systems in cybernetics. Technique, Kiev, 392 p (in Russian)"},{"key":"36_CR2","first-page":"177","volume":"4","author":"AG Ivakhnenko","year":"1994","unstructured":"Ivakhnenko AG, Ivakhnenko GA, Muller JA (1994) Selforganisation of neuronets with active neurons. Pattern Recogn Image Anal 4:177\u2013188 (in Russian)","journal-title":"Pattern Recogn Image Anal"},{"key":"36_CR3","unstructured":"Anastasakis L, Mort N (2001) The development of self-organization technique in modelling: a review of the group method of data handling (GMDH). Research report no 813, Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield"},{"issue":"3","key":"36_CR4","doi-asserted-by":"publisher","first-page":"232","DOI":"10.18267\/j.pep.398","volume":"2011","author":"J Tau\u0161er","year":"2011","unstructured":"Tau\u0161er J, Buryan P (2011) Exchange rate predictions in international financial management by enhanced GMDH algorithm. Prague Econ Pap Univ Econ Prague 2011(3):232\u2013249","journal-title":"Prague Econ Pap Univ Econ Prague"},{"key":"36_CR5","unstructured":"Park HS, Oh SK, Ahn TC, Pedrycz WC (1999) A study on multi-layer fuzzy polynomial inference system based on extended GMDH algorithm. In: Proceedings of the 1999 IEEE international conference on fuzzy systems, FUZZ-IEEE 1999, vol 1, pp 354\u2013359"},{"issue":"3","key":"36_CR6","first-page":"25","volume":"25","author":"AG Ivakhnenko","year":"1992","unstructured":"Ivakhnenko AG, Zholnarskiy AA (1992) Estimating the coefficients of polynomials in parametric GMDH algorithms by the improved instrumental variables method. J Autom Inf Sci c\/c Avtomatika 25(3):25\u201332 (in Russian)","journal-title":"J Autom Inf Sci c\/c Avtomatika"},{"issue":"5","key":"36_CR7","first-page":"1","volume":"17","author":"AP Sarychev","year":"1984","unstructured":"Sarychev AP (1984) Stable estimation of the coefficients in multilayer GMDH algorithms. Sov Autom Control c\/c Avtomatika 17(5):1\u20135 (in Russian)","journal-title":"Sov Autom Control c\/c Avtomatika"},{"key":"36_CR8","unstructured":"Parker RG, Tummala MJ (1992) Identification of volterra systems with a polynomial neural network. In: Proceedings of the 1992 IEEE international conference on acoustics \u2013 speech and signal processing, ICASSP 1992, vol 4, pp 561\u2013564"},{"key":"36_CR9","unstructured":"Dolenko SA, Orlov YV, Persiantsev IG (1996) Practical implementation and use of group method of data handling (GMDH): prospects and problems. In: Proceedings of the 2nd international conference on adaptive computing in engineering design and control - ACEDC 1996. PEDC, University of Plymouth, UK, pp 291\u2013293"},{"key":"36_CR10","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/978-3-642-01530-4","volume-title":"Hybrid self-organizing modeling systems","author":"GC Onwubolu","year":"2009","unstructured":"Onwubolu GC (2009) Hybrid self-organizing modeling systems. Springer, Berlin, pp 233\u2013280"},{"key":"36_CR11","unstructured":"Moroz OV, Stepashko VS (2015) An overview of hybrid structures of GMDH-like neural networks and genetic algorithms. Inductive modeling of complex systems. K.: MNNTS IT\n                    \n                      \n                    \n                    $$\\bullet $$\n                  S NAN ta MON Ukrayiny, Vyp 7, pp 173\u2013191 (in Ukrainian)"},{"key":"36_CR12","series-title":"Studies in computational intelligence","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-642-01530-4_5","volume-title":"Hybrid self-organizing modeling systems","author":"A Sharma","year":"2009","unstructured":"Sharma A, Onwubolu G (2009) Hybrid particle swarm optimization and GMDH system. In: Onwubolu GC (ed) Hybrid self-organizing modeling systems, vol 211. Studies in computational intelligence. Springer, Heidelberg, pp 193\u2013231"},{"key":"36_CR13","unstructured":"Onwubolu GC (2007) Design of hybrid differential evolution and group method in data handling for modeling. In: International workshop on inductive modeling, IWIM 2007, Prague, Czech, 23\u201326 September, pp 87\u201395"},{"issue":"2","key":"36_CR14","first-page":"87","volume":"13","author":"C Ferreira","year":"2001","unstructured":"Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87\u2013129","journal-title":"Complex Syst"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Dasgupta D (1999) Artificial immune systems and their applications. Springer, Heidelberg, 306 p","DOI":"10.1007\/978-3-642-59901-9"},{"key":"36_CR16","unstructured":"De Castro LN, Timmis JC (2002) Artificial immune systems: a new computational intelligence approach. Springer, Heidelberg, 357 p"},{"key":"36_CR17","unstructured":"De Castro LN, Von Zuben FJ (2001) The clonal selection algorithm with engineering applications. In: Proceedings of GECCO 2000, pp 36\u201337"},{"key":"36_CR18","unstructured":"Fefelov AO, Lytvynenko VI, Bidyuk PI (2006) Cooperative algorithm for solving the problem of signal approximation, processing of signals, images and recognition of images: VIII Allukrainian mizhnar conference, 11\u201315 October 2006, pp 41\u201344 (in Ukrainian)"},{"key":"36_CR19","unstructured":"Artificial neural network and computational intelligence forecasting competition. \n                    http:\/\/www.neural-forecasting-competition.com\/"},{"issue":"73","key":"36_CR20","doi-asserted-by":"publisher","first-page":"2540","DOI":"10.1016\/j.neucom.2010.06.004","volume":"2010","author":"M Ardalani-Farsa","year":"2010","unstructured":"Ardalani-Farsa M, Zolfaghari S (2010) Chaotic time series prediction with residual analysis method using hybrid Elman-NARX neural networks. Neurocomputing 2010(73):2540\u20132553","journal-title":"Neurocomputing"},{"key":"36_CR21","first-page":"171","volume":"24","author":"MV Shcherbakov","year":"2013","unstructured":"Shcherbakov MV, Brebels AC, Shcherbakova NL, Tyukov AP, Janovsky TA, Kamaev VA (2013) A survey of forecast error measures. World Appl Sci J 24:171\u2013176","journal-title":"World Appl Sci J"}],"container-title":["Advances in Intelligent Systems and Computing","Lecture Notes in Computational Intelligence and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-26474-1_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T04:17:18Z","timestamp":1563855438000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-26474-1_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,24]]},"ISBN":["9783030264734","9783030264741"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-26474-1_36","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2019,7,24]]},"assertion":[{"value":"24 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISDMCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Scientific Conference \u201cIntellectual Systems of Decision Making and Problem of Computational Intelligence\u201d","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zalizniy Port","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ukraine","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isdmci2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/isdmci.org.ua\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}