{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:03:22Z","timestamp":1760609002576},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319594149"},{"type":"electronic","value":"9783319594156"}],"license":[{"start":{"date-parts":[[2017,5,31]],"date-time":"2017-05-31T00:00:00Z","timestamp":1496188800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-59415-6_5","type":"book-chapter","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T04:42:30Z","timestamp":1496119350000},"page":"49-59","source":"Crossref","is-referenced-by-count":4,"title":["Deep Stacking Convex Neuro-Fuzzy System and Its On-line Learning"],"prefix":"10.1007","author":[{"given":"Yevgeniy","family":"Bodyanskiy","sequence":"first","affiliation":[]},{"given":"Olena","family":"Vynokurova","sequence":"additional","affiliation":[]},{"given":"Iryna","family":"Pliss","sequence":"additional","affiliation":[]},{"given":"Dmytro","family":"Peleshko","sequence":"additional","affiliation":[]},{"given":"Yuriy","family":"Rashkevych","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,5,31]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521, 436\u2013444 (2015)","journal-title":"Nature"},{"key":"5_CR2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"key":"5_CR3","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, New York (2016)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Peleshko, D., Ivanov, Y., Sharov, B., Izonin, I., Borzov, Y.: Design and implementation of visitors queue density analysis and registration method for retail videosurveillance purposes. In: 2016 IEEE First International Conference on Data Stream Mining and Processing, Lviv, Ukraine, pp. 159\u2013162 (2016)","DOI":"10.1109\/DSMP.2016.7583531"},{"key":"5_CR5","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-76288-1","volume-title":"Computational Intelligence: Methods and Techniques","author":"L Rutkowski","year":"2008","unstructured":"Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Berlin (2008)"},{"key":"5_CR6","series-title":"A Methodological Introduction","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4471-5013-8","volume-title":"Computational Intelligence","author":"R Kruse","year":"2013","unstructured":"Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P.: Computational Intelligence. A Methodological Introduction. Springer, Berlin (2013)"},{"key":"5_CR7","volume-title":"Neural Networks and Statistical Learning","author":"K-L Du","year":"2014","unstructured":"Du, K.-L., Swamy, M.N.S.: Neural Networks and Statistical Learning. Springer, London (2014)"},{"key":"5_CR8","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-662-04323-3","volume-title":"Nonlinear Systems Identification","author":"O Nelles","year":"2001","unstructured":"Nelles, O.: Nonlinear Systems Identification. Springer, Berlin (2001)"},{"key":"5_CR9","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference and Prediction","author":"TJ Hastie","year":"2009","unstructured":"Hastie, T.J., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer Science+Business Media, LLC, N.Y. (2009)"},{"issue":"1","key":"5_CR10","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TSMC.1985.6313399","volume":"15","author":"T Takagi","year":"1985","unstructured":"Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116\u2013132 (1985)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"3","key":"5_CR11","first-page":"116","volume":"23","author":"RJ-S Jang","year":"1993","unstructured":"Jang, R.J.-S.: ANFIS: adaptive network based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23(3), 116\u2013132 (1993)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"2","key":"5_CR12","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.1109\/TIE.2008.924018","volume":"55","author":"R Abiyev","year":"2008","unstructured":"Abiyev, R., Kaynak, O.: Fuzzy wavelet neural networks for identification and control of dynamic plants \u2013 a novel structure and a comparative study. IEEE Trans. Ind. Electron. 55(2), 3133\u20133140 (2008)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"5_CR13","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.ins.2012.07.044","volume":"220","author":"Y Bodyanskiy","year":"2013","unstructured":"Bodyanskiy, Y., Vynokurova, O.: Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification. Inf. Sci. 220, 170\u2013179 (2013)","journal-title":"Inf. Sci."},{"key":"5_CR14","unstructured":"Fahlman, S.E., Lebiere, C.: The cascade-correlation learning architecture. In: Advances Neural Information Processing Systems, pp. 524\u2013532. Morgan Kaufman, San Mateo (1990)"},{"issue":"8","key":"5_CR15","first-page":"11","volume":"6","author":"Y Bodyanskiy","year":"2014","unstructured":"Bodyanskiy, Y., Tyshchenko, O., Kopaliani, D.: A multidimensional cascade neuro-fuzzy system with neuron pool optimization in each cascade. Int. J. Inf. Technol. Comput. Sci. 6(8), 11\u201317 (2014)","journal-title":"Int. J. Inf. Technol. Comput. Sci."},{"key":"5_CR16","unstructured":"Bodyanskiy, Y., Tyshchenko, O.: Fast deep learning for cascade neuro-fuzzy system. In: 2nd International Workshop and Networking Event \u201cAdvances in Data Science\u201d, Holny Mejera, Poland, pp. 18\u201319 (2016)"},{"key":"5_CR17","unstructured":"Bodyanskiy, Y., Pliss, I., Peleshko, D., Vynokurova, O.: Adaptive deep learning of multivariate hybrid system of computational intelligence. In: VIIth International Conference on Decision-Making Theory, Uzhhorod, Ukraine, pp. 58\u201359 (2016). (In Ukrainian)"},{"key":"5_CR18","volume-title":"Adaptive Fuzzy Systems and Control: Design and Stability Analysis","author":"L-X Wang","year":"1994","unstructured":"Wang, L.-X.: Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Prentice Hall, Upper-Saddle River (1994)"},{"key":"5_CR19","unstructured":"Yamakawa, T., Uchino, E., Miki, T., Kusanagi, H.: A neo-fuzzy neuron and its applications to system identification and prediction of the system behavior. In: 2nd International Conference on Fuzzy Logic and Neural Networks, IIZUKA 1992, Iizuka, Japan, pp. 477\u2013483 (1992)"},{"key":"5_CR20","unstructured":"Miki, T., Yamakawa, T.: Analog implementation of neo-fuzzy neuron and its on-board learning. In: Mastorakis, N.E. (ed.) Computational Intelligence and Application, pp. 144\u2013149. WSES Press, Piraeus (1999)"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Bodyanskiy, Y., Mulesa, P., Setlak, G., Pliss, I., Vynokurova, O.: Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks. In: 2016 IEEE First International Conference on Data Stream Mining and Processing (DSMP), pp. 257\u2013262 (2016)","DOI":"10.1109\/DSMP.2016.7583555"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Bodyanskiy, Y., Setlak, G., Pliss, I.P., Vynokurova, O.: Hybrid neuro-neo-fuzzy system and its adaptive learning algorithm. In: Xth IEEE International Science and Technology Conference on Computer Science and Information Technologies, Lviv, Ukraine, pp. 111\u2013114 (2015)","DOI":"10.1109\/STC-CSIT.2015.7325445"},{"issue":"3","key":"5_CR23","first-page":"121","volume":"7","author":"S-H Lee","year":"2013","unstructured":"Lee, S.-H., Lee, J.-G., Moon, K.-I.: Smart home security system using multiple ANFIS. Int. J. Smart Home 7(3), 121\u2013132 (2013)","journal-title":"Int. J. Smart Home"},{"key":"5_CR24","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.neucom.2016.12.042","volume":"230","author":"Y Bodyanskiy","year":"2017","unstructured":"Bodyanskiy, Y., Peleshko, D., Setlak, G., Mulesa, P., Vynokurova, O.: Adaptive multivariate generalized additive neuro-fuzzy systems and its on-board fast learning. Neurocomputing 230, 409\u2013416 (2017)","journal-title":"Neurocomputing"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Bodyanskiy, Y., Peleshko, D., Tatarinova, Yu., Vynokurova, O: Architecture of hybrid generalized additive neuro-fuzzy system in modelling technological process. In: XIIIth IEEE International Conference on The Experimental of Designing and Application of CAD Systems in Microelectronics, Lviv-Polyana, Ukraine, pp. 333\u2013335 (2015)","DOI":"10.1109\/IDAACS.2015.7340753"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Bodyanskiy, Y., Setlak, G., Peleshko, D., Vynokurova, O.: Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms. In: The 8th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Warsaw, Poland, pp. 328\u2013333 (2015)","DOI":"10.1109\/IDAACS.2015.7340753"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Landim, R.P., Rodrignes, B., Silva, S.R., Matos, W.: A neo-fuzzy-neuron with real-time training applied to flux observer for an induction motor. In: Vth Brasilian Symposium on Neural Networks, pp. 67\u201372. IEEE Computer Society, Los Alamitos (1998)","DOI":"10.1109\/SBRN.1998.730996"},{"key":"5_CR28","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.asoc.2013.03.022","volume":"14","author":"AM Silva","year":"2014","unstructured":"Silva, A.M., Caminhas, W., Lemos, A., Gomide, F.: A fast learning algorithm for evolving neo-fuzzy neuron. Appl. Soft Comput. J. 14, 194\u2013209 (2014)","journal-title":"Appl. Soft Comput. J."},{"key":"5_CR29","volume-title":"Generalized Additive Models","author":"TJ Hastie","year":"1990","unstructured":"Hastie, T.J., Tibshrani, R.J.: Generalized Additive Models. Chapman and Hall, London (1990)"},{"key":"5_CR30","unstructured":"Bossley, K.M., Brown, M., Harris, C.J.: Neuro-fuzzy model construction for modelling of non-linear processes. In: 3rd European Control Conference, Italy, vol. 3, pp. 2438\u20132443 (1995)"}],"container-title":["Advances in Intelligent Systems and Computing","Advances in Dependability Engineering of Complex Systems"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-59415-6_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T03:09:53Z","timestamp":1569380993000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-59415-6_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,31]]},"ISBN":["9783319594149","9783319594156"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-59415-6_5","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2017,5,31]]}}}