{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:34:40Z","timestamp":1764174880878,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030595340"},{"type":"electronic","value":"9783030595357"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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\/978-3-030-59535-7_10","type":"book-chapter","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T07:03:52Z","timestamp":1600671832000},"page":"134-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Incremental Structure-Evolving Intelligent Systems with Advanced Interpretational Properties"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7103-4820","authenticated-orcid":false,"given":"Sergey","family":"Kovalev","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Kolodenkova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6161-4709","authenticated-orcid":false,"given":"Andrey","family":"Sukhanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"issue":"5","key":"10_CR1","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s00138-009-0211-1","volume":"21","author":"C Eitzinger","year":"2010","unstructured":"Eitzinger, C., et al.: Assessment of the influence of adaptive components in trainable surface inspection systems. Mach. Vis. Appl. 21(5), 613\u2013626 (2010)","journal-title":"Mach. Vis. Appl."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Angelov, P.: Evolving takagi-sugeno fuzzy systems from streaming data (eTS+). In: Evolving Intelligent Systems: Methodology and Applications, vol. 12, p. 21. Wiley Online Library (2010)","DOI":"10.1002\/9780470569962.ch2"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Kasabov, N., Filev, D.: Evolving intelligent systems: methods, learning, & applications. In: 2006 International symposium on evolving fuzzy systems, pp. 8\u201318. IEEE (2006)","DOI":"10.1109\/ISEFS.2006.251185"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"106095","DOI":"10.1016\/j.asoc.2020.106095","volume":"89","author":"P Cort\u00e9s-Antonio","year":"2020","unstructured":"Cort\u00e9s-Antonio, P., et al.: Learning rules for Sugeno ANFIS with parametric conjunction operations. Appl. Soft Comput. 89, 106095 (2020)","journal-title":"Appl. Soft Comput."},{"key":"10_CR5","doi-asserted-by":"publisher","DOI":"10.1201\/EBK1439826119","volume-title":"Knowledge Discovery from Data Streams","author":"J Gama","year":"2010","unstructured":"Gama, J.: Knowledge Discovery from Data Streams. CRC Press, Boca Raton (2010)"},{"key":"10_CR6","doi-asserted-by":"publisher","unstructured":"Lughofer, E.: Flexible evolving fuzzy inference systems from data streams (FLEXFIS++). In: Sayed-Mouchaweh, M., Lughofer, E. (eds.) Learning in Non-Stationary Environments, pp. 205\u2013245. Springer, New York (2012). \nhttps:\/\/doi.org\/10.1007\/978-1-4419-8020-5_9","DOI":"10.1007\/978-1-4419-8020-5_9"},{"issue":"2","key":"10_CR7","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1007\/s13369-013-0697-8","volume":"39","author":"H Shahparast","year":"2014","unstructured":"Shahparast, H., Jahromi, M.Z., Taheri, M., Hamzeloo, S.: A novel weight adjustment method for handling concept-drift in data stream classification. Arabian J. Sci. Eng. 39(2), 799\u2013807 (2014)","journal-title":"Arabian J. Sci. Eng."},{"issue":"2","key":"10_CR8","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.tins.2004.12.003","volume":"28","author":"WC Abraham","year":"2005","unstructured":"Abraham, W.C., Robins, A.: Memory retention\u2013the synaptic stability versus plasticity dilemma. Trends Neurosci. 28(2), 73\u201378 (2005)","journal-title":"Trends Neurosci."},{"key":"10_CR9","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-68321-8_24","volume-title":"Proceedings of the Second International Scientific Conference \u201cIntelligent Information Technologies for Industry\u201d (IITI\u201917)","author":"SM Kovalev","year":"2018","unstructured":"Kovalev, S.M., Sukhanov, A.V., Sukhanova, M.V., Sokolov, S.V.: Adaptive approach for anomaly detection in temporal data based on immune double-plasticity principle. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds.) IITI 2017. AISC, vol. 679, pp. 234\u2013243. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-319-68321-8_24"},{"key":"10_CR10","doi-asserted-by":"publisher","unstructured":"Korbicz, J., Koscielny, J.M., Kowalczuk, Z., Cholewa, W.: Fault diagnosis: models, artificial intelligence, applications. Springer, Heidelberg (2012). \nhttps:\/\/doi.org\/10.1007\/978-3-642-18615-8","DOI":"10.1007\/978-3-642-18615-8"},{"key":"10_CR11","doi-asserted-by":"publisher","unstructured":"Lughofer, E., Eitzinger, C., Guardiola, C.: Online quality control with flexible evolving fuzzy systems. In: Sayed-Mouchaweh, M., Lughofer, E. (eds.) Learning in Non-Stationary Environments, pp. 375\u2013406. Springer, New York (2012). \nhttps:\/\/doi.org\/10.1007\/978-1-4419-8020-5_14","DOI":"10.1007\/978-1-4419-8020-5_14"},{"issue":"3","key":"10_CR12","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1016\/j.asoc.2007.02.022","volume":"8","author":"CD Stylios","year":"2008","unstructured":"Stylios, C.D., Georgopoulos, V.C., Malandraki, G.A., Chouliara, S.: Fuzzy cognitive map architectures for medical decision support systems. Appl. Soft Comput. 8(3), 1243\u20131251 (2008)","journal-title":"Appl. Soft Comput."},{"issue":"23","key":"10_CR13","doi-asserted-by":"publisher","first-page":"5123","DOI":"10.1016\/j.ins.2011.07.012","volume":"181","author":"E Lughofer","year":"2011","unstructured":"Lughofer, E., Trawi\u0144ski, B., Trawi\u0144ski, K., Kempa, O., Lasota, T.: On employing fuzzy modeling algorithms for the valuation of residential premises. Inf. Sci. 181(23), 5123\u20135142 (2011)","journal-title":"Inf. Sci."},{"key":"10_CR14","doi-asserted-by":"publisher","unstructured":"Leite, D., Costa, P., Gomide, F.: Interval approach for evolving granular system modeling. In: Sayed-Mouchaweh, M., Lughofer, E. (eds.) Learning in non-stationary environments, pp. 271\u2013300. Springer (2012). \nhttps:\/\/doi.org\/10.1007\/978-1-4419-8020-5_11","DOI":"10.1007\/978-1-4419-8020-5_11"},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.ins.2013.07.002","volume":"251","author":"E Lughofer","year":"2013","unstructured":"Lughofer, E.: On-line assurance of interpretability criteria in evolving fuzzy systems\u2013achievements, new concepts and open issues. Inf. Sci. 251, 22\u201346 (2013)","journal-title":"Inf. Sci."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Lima, E., Hell, M., Ballini, R., Gomide, F.: Evolving fuzzy modeling using participatory learning. Evolving intelligent systems: methodology and applications, pp. 67\u201386 (2010)","DOI":"10.1002\/9780470569962.ch4"},{"key":"10_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/978-3-642-15246-7_49","volume-title":"PRICAI 2010: Trends in Artificial Intelligence","author":"SW Tung","year":"2010","unstructured":"Tung, S.W., Quek, C., Guan, C.: An evolving type-2 neural fuzzy inference system. In: Zhang, B.-T., Orgun, Mehmet A. (eds.) PRICAI 2010. LNCS (LNAI), vol. 6230, pp. 535\u2013546. Springer, Heidelberg (2010). \nhttps:\/\/doi.org\/10.1007\/978-3-642-15246-7_49"},{"issue":"1","key":"10_CR18","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1109\/TSMCB.2003.817053","volume":"34","author":"PP Angelov","year":"2004","unstructured":"Angelov, P.P., Filev, D.P.: An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 484\u2013498 (2004)","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"issue":"9","key":"10_CR19","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1016\/j.fss.2005.12.011","volume":"157","author":"HJ Rong","year":"2006","unstructured":"Rong, H.J., Sundararajan, N., Huang, G.B., Saratchandran, P.: Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets Syst. 157(9), 1260\u20131275 (2006)","journal-title":"Fuzzy Sets Syst."},{"issue":"1","key":"10_CR20","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/91.660805","volume":"6","author":"CF Juang","year":"1998","unstructured":"Juang, C.F., Lin, C.T.: An online self-constructing neural fuzzy inference network and its applications. IEEE Trans. Fuzzy Syst. 6(1), 12\u201332 (1998)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"1","key":"10_CR21","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s12530-012-9045-6","volume":"3","author":"G Leng","year":"2012","unstructured":"Leng, G., Zeng, X.J., Keane, J.A.: An improved approach of self-organising fuzzy neural network based on similarity measures. Evol. Syst. 3(1), 19\u201330 (2012)","journal-title":"Evol. Syst."},{"key":"10_CR22","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s12530-020-09334-5","volume":"11","author":"D Leite","year":"2020","unstructured":"Leite, D., \u0160krjanc, I., Gomide, F.: An overview on evolving systems and learning from stream data. Evol. Syst. 11, 181\u2013198 (2020)","journal-title":"Evol. Syst."},{"issue":"2","key":"10_CR23","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0165-0114(99)00109-8","volume":"118","author":"A Fiordaliso","year":"2001","unstructured":"Fiordaliso, A.: A constrained Takagi-Sugeno fuzzy system that allows for better interpretation and analysis. Fuzzy Sets Syst. 118(2), 307\u2013318 (2001)","journal-title":"Fuzzy Sets Syst."},{"key":"10_CR24","doi-asserted-by":"publisher","unstructured":"Setnes, M.: Simplification and reduction of fuzzy rules. In: Casillas, J., Cord\u00f3n, O., Herrera, F., Magdalena, L. (eds.) Interpretability Issues in Fuzzy Modeling, vol 128, pp. 278\u2013302. Springer, Heidelberg (2003). \nhttps:\/\/doi.org\/10.1007\/978-3-540-37057-4_12","DOI":"10.1007\/978-3-540-37057-4_12"},{"issue":"20","key":"10_CR25","doi-asserted-by":"publisher","first-page":"4340","DOI":"10.1016\/j.ins.2011.02.021","volume":"181","author":"MJ Gacto","year":"2011","unstructured":"Gacto, M.J., Alcala, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181(20), 4340\u20134360 (2011)","journal-title":"Inf. Sci."},{"issue":"2","key":"10_CR26","first-page":"99","volume":"25","author":"S Koenig","year":"2004","unstructured":"Koenig, S., Likhachev, M., Liu, Y., Furcy, D.: Incremental heuristic search in artificial intelligence. Artif. Intell. Mag. 25(2), 99\u2013112 (2004)","journal-title":"Artif. Intell. Mag."},{"key":"10_CR27","unstructured":"Filev, D., Yager, R.R.: Learning OWA operator weights from data. In: Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, pp. 468\u2013473. IEEE (1994)"},{"issue":"8","key":"10_CR28","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1109\/21.299710","volume":"24","author":"RR Yager","year":"1994","unstructured":"Yager, R.R., Filev, D.P.: Approximate clustering via the mountain method. IEEE Trans. Syst. Man Cybern. 24(8), 1279\u20131284 (1994)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10_CR29","unstructured":"Yager, R.R., Filev, D.P.: Essentials of fuzzy modeling and control, New York, vol. 388 (1994)"},{"key":"10_CR30","doi-asserted-by":"publisher","unstructured":"Lughofer, E.: Evolving Fuzzy Systems-Methodologies, Advanced Concepts and Applications, vol. 53. Springer, Heidelberg (2011). \nhttps:\/\/doi.org\/10.1007\/978-3-642-18087-3","DOI":"10.1007\/978-3-642-18087-3"},{"issue":"23","key":"10_CR31","doi-asserted-by":"publisher","first-page":"3160","DOI":"10.1016\/j.fss.2008.06.019","volume":"159","author":"P Angelov","year":"2008","unstructured":"Angelov, P., Lughofer, E., Zhou, X.: Evolving fuzzy classifiers using different model architectures. Fuzzy Sets Syst. 159(23), 3160\u20133182 (2008)","journal-title":"Fuzzy Sets Syst."},{"issue":"7","key":"10_CR32","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1109\/JPROC.2002.801448","volume":"90","author":"A Elgammal","year":"2002","unstructured":"Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90(7), 1151\u20131163 (2002)","journal-title":"Proc. IEEE"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Yager, R.R., Fileu, D.P.: Learning of fuzzy rules by mountain clustering. In: Applications of Fuzzy Logic Technology, vol. 2061, pp. 246\u2013254. International Society for Optics and Photonics (1993)","DOI":"10.1117\/12.165030"},{"issue":"3","key":"10_CR34","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3233\/IFS-1994-2306","volume":"2","author":"SL Chiu","year":"1994","unstructured":"Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2(3), 267\u2013278 (1994)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"3","key":"10_CR35","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.ijar.2003.08.006","volume":"35","author":"P Angelov","year":"2004","unstructured":"Angelov, P.: An approach for fuzzy rule-base adaptation using on-line clustering. Int. J. Approximate Reasoning 35(3), 275\u2013289 (2004)","journal-title":"Int. J. Approximate Reasoning"},{"issue":"3","key":"10_CR36","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s12530-011-9032-3","volume":"2","author":"E Lughofer","year":"2011","unstructured":"Lughofer, E., Bouchot, J.L., Shaker, A.: On-line elimination of local redundancies in evolving fuzzy systems. Evol. Syst. 2(3), 165\u2013187 (2011)","journal-title":"Evol. Syst."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59535-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T23:23:47Z","timestamp":1601594627000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-59535-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030595340","9783030595357"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59535-7_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"22 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russian Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Moscow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rcai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/caics.ru\/en_raai","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"140","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}