{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:15:49Z","timestamp":1742919349384,"version":"3.40.3"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031425042"},{"type":"electronic","value":"9783031425059"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-42505-9_2","type":"book-chapter","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T07:03:11Z","timestamp":1694588591000},"page":"12-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["On Speeding up the Levenberg-Marquardt Learning Algorithm"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1769-3934","authenticated-orcid":false,"given":"Jaros\u0142aw","family":"Bilski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7683-9051","authenticated-orcid":false,"given":"Barosz","family":"Kowalczyk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1326-3374","authenticated-orcid":false,"given":"Jacek","family":"Smola\u0327g","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Bartczuk, \u0141., Przyby\u0142, A., Cpa\u0142ka, K.: A new approach to nonlinear modelling of dynamic systems based on fuzzy rules. Int. J. Appl. Math. Comput. Sci. (AMCS) 26(3), 603\u2013621 (2016)","DOI":"10.1515\/amcs-2016-0042"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Bilski, J., Kowalczyk, B., Smola\u0327g, J., Grzanek, K., Izonin, I.: Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks. J. Artif. Intell. Soft Comput. Res. 12(2), 45\u201361 (2023)","DOI":"10.2478\/jaiscr-2023-0006"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Bilski, J., Litwi\u0144ski, S., Smola\u0327g, J.: Parallel realisation of QR algorithm for neural networks learning. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 158\u2013165. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-24844-6_19","DOI":"10.1007\/978-3-540-24844-6_19"},{"issue":"1","key":"2_CR4","first-page":"101","volume":"15","author":"J Bilski","year":"2005","unstructured":"Bilski, J.: The UD RLS algorithm for training the feedforward neural networks. Int. J. Appl. Math. Comput. Sci. 15(1), 101\u2013109 (2005)","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"2_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-540-69731-2_2","volume-title":"Artificial Intelligence and Soft Computing \u2013 ICAISC 2008","author":"J Bilski","year":"2008","unstructured":"Bilski, J., Smola\u0327g, J.: Parallel realisation of the recurrent RTRN neural network learning. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 11\u201316. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-69731-2_2"},{"key":"2_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-642-13232-2_3","volume-title":"Artifical Intelligence and Soft Computing","author":"J Bilski","year":"2010","unstructured":"Bilski, J., Smola\u0327g, J.: Parallel realisation of the recurrent Elman neural network learning. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6114, pp. 19\u201325. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13232-2_3"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Bilski, J., Smola\u0327g, J.: Parallel realisation of the recurrent multi layer perceptron learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS (LNAI), vol. 7267, pp. 12\u201320. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-29347-4_2","DOI":"10.1007\/978-3-642-29347-4_2"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Bilski, J., Smola\u0327g, J.: Parallel approach to learning of the recurrent Jordan neural network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7894, pp. 32\u201340. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38658-9_3","DOI":"10.1007\/978-3-642-38658-9_3"},{"key":"2_CR9","unstructured":"Bilski, J.: Parallel Structures for Feedforward and Dynamical Neural Networks. AOW EXIT (2013). (in Polish)"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Bilski, J., Smola\u0327g, J., Galushkin, A.I.: The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS (LNAI), vol. 8467, pp. 12\u201321. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07173-2_2","DOI":"10.1007\/978-3-319-07173-2_2"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Bilski, J., Smola\u0327g, J.: Parallel architectures for learning the RTRN and Elman dynamic neural networks. IEEE Trans. Parallel Distrib. Syst. 26(9), 2561\u20132570 (2014). https:\/\/doi.org\/10.1109\/TPDS.2014.2357019","DOI":"10.1109\/TPDS.2014.2357019"},{"issue":"4","key":"2_CR12","doi-asserted-by":"publisher","first-page":"299","DOI":"10.2478\/jaiscr-2020-0020","volume":"10","author":"J Bilski","year":"2020","unstructured":"Bilski, J., Kowalczyk, B., Marchlewska, A., \u017burada, J.M.: Local Levenberg-Marquardt algorithm for learning feedforward neural networks. J. Artif. Intell. Soft Comput. Res. 10(4), 299\u2013316 (2020). https:\/\/doi.org\/10.2478\/jaiscr-2020-0020","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"4","key":"2_CR13","doi-asserted-by":"publisher","first-page":"287","DOI":"10.2478\/jaiscr-2021-0017","volume":"11","author":"J Bilski","year":"2021","unstructured":"Bilski, J., Kowalczyk, B., Marja\u0144ski, A., Gandor, M., \u017burada, J.M.: A novel fast feedforward neural networks training algorithm. J. Artif. Intell. Soft Comput. Res. 11(4), 287\u2013306 (2021). https:\/\/doi.org\/10.2478\/jaiscr-2021-0017","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"3","key":"2_CR14","doi-asserted-by":"publisher","first-page":"181","DOI":"10.2478\/jaiscr-2022-0012","volume":"12","author":"J Bilski","year":"2022","unstructured":"Bilski, J., Kowalczyk, B., Kisiel-Dorohinicki, M., Siwocha, A., \u017burada, J.M.: Towards a very fast feedforward multilayer neural networks training algorithm. J. Artif. Intell. Soft Comput. Res. 12(3), 181\u2013195 (2022). https:\/\/doi.org\/10.2478\/jaiscr-2022-0012","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Bilski, J., Rutkowski, L., Smola\u0327g, J., Tao, D.: A novel method for speed training acceleration of recurrent neural networks. Inf. Sci. 553, 266\u2013279 (2021). https:\/\/doi.org\/10.1016\/j.ins.2020.10.025","DOI":"10.1016\/j.ins.2020.10.025"},{"issue":"1","key":"2_CR16","doi-asserted-by":"publisher","first-page":"5","DOI":"10.2478\/jaiscr-2014-0021","volume":"4","author":"JL Chu","year":"2014","unstructured":"Chu, J.L., Krzy\u017cak, A.: The recognition of partially occluded objects with support vector machines, convolutional neural networks, and deep belief networks. J. Artif. Intell. Soft Comput. Res. 4(1), 5\u201319 (2014)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Cpa\u0142ka, K., Rutkowski, L.: Flexible Takagi-Sugeno fuzzy systems. In: Proceedings of the International Joint Conference on Neural Networks, Montreal, pp. 1764\u20131769 (2005)","DOI":"10.1109\/IJCNN.2005.1556147"},{"key":"2_CR18","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.neucom.2013.12.031","volume":"135","author":"K Cpa\u0142ka","year":"2014","unstructured":"Cpa\u0142ka, K., \u0141apa, K., Przyby\u0142, A., Zalasi\u0144ski, M.: A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects. Neurocomputing 135, 203\u2013217 (2014)","journal-title":"Neurocomputing"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Cpa\u0142ka, K., Rebrova, O., Nowicki, R., et al.: On design of flexible neuro-fuzzy systems for nonlinear modelling. Int. J. Gen. Syst. 42(6), Special Issue: SI, 706\u2013720 (2013)","DOI":"10.1080\/03081079.2013.798912"},{"key":"2_CR20","unstructured":"Fahlman, S.: Faster learning variations on backpropagation: an empirical study. In: Proceedings of Connectionist Models Summer School, Los Atos (1988)"},{"issue":"3","key":"2_CR21","doi-asserted-by":"publisher","first-page":"243","DOI":"10.2478\/jaiscr-2021-0015","volume":"11","author":"P Dziwi\u0144ski","year":"2021","unstructured":"Dziwi\u0144ski, P., Przyby\u0142, A., Trippner, P., Paszkowski, J., Havashi, Y.: Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm. J. Artif. Intell. Soft Comput. Res. 11(3), 243\u2013266 (2021). https:\/\/doi.org\/10.2478\/jaiscr-2021-0015","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"2_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-030-61534-5_29","volume-title":"Artificial Intelligence and Soft Computing","author":"M Gabryel","year":"2020","unstructured":"Gabryel, M., Przybyszewski, K.: Methods of searching for similar device fingerprints using changes in unstable parameters. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2020. LNCS (LNAI), vol. 12416, pp. 325\u2013335. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61534-5_29"},{"key":"2_CR23","doi-asserted-by":"publisher","first-page":"331","DOI":"10.2478\/jaiscr-2021-0020","volume":"11","author":"M Gabryel","year":"2021","unstructured":"Gabryel, M., Scherer, M.M., Su\u0142kowski, \u0141, Dama\u0161evi\u010dius, R.: Decision making support system for managing advertisers by Ad fraud detection. J. Artif. Intell. Soft Comput. Res. 11, 331\u2013339 (2021)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"2_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1007\/978-3-030-87897-9_40","volume-title":"Artificial Intelligence and Soft Computing","author":"M Gabryel","year":"2021","unstructured":"Gabryel, M., Koci\u0107, M.: Application of a neural network to generate the hash code for a device fingerprint. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2021. LNCS (LNAI), vol. 12855, pp. 456\u2013463. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87897-9_40"},{"issue":"6","key":"2_CR25","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1109\/72.329697","volume":"5","author":"MT Hagan","year":"1994","unstructured":"Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks 5(6), 989\u2013993 (1994)","journal-title":"IEEE Trans. Neural Networks"},{"issue":"2","key":"2_CR26","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2478\/jaiscr-2021-0007","volume":"11","author":"M Kopczy\u0144ski","year":"2021","unstructured":"Kopczy\u0144ski, M., Grzes, T.: Hardware rough set processor parallel architecture in FPGA for finding core in big datasets. J. Artif. Intell. Soft Comput. Res. 11(2), 99\u2013110 (2021)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"2_CR27","first-page":"265","volume":"5097","author":"M Korytkowski","year":"2008","unstructured":"Korytkowski, M., Rutkowski, L., Scherer, R.: From ensemble of fuzzy classifiers to single fuzzy rule base classifier. LNAI 5097, 265\u2013272 (2008)","journal-title":"LNAI"},{"key":"2_CR28","first-page":"114","volume":"6113","author":"M Korytkowski","year":"2010","unstructured":"Korytkowski, M., Scherer, R.: Negative correlation learning of neuro-fuzzy system. LNAI 6113, 114\u2013119 (2010)","journal-title":"LNAI"},{"key":"2_CR29","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.ins.2021.12.016","volume":"587","author":"M Kordos","year":"2021","unstructured":"Kordos, M., Blachnik, M., Scherer, R.: Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems. Inf. Sci. 587, 23\u201340 (2021)","journal-title":"Inf. Sci."},{"key":"2_CR30","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/978-3-642-38610-7_48","volume-title":"Artificial Intelligence and Soft Computing","author":"K \u0141apa","year":"2013","unstructured":"\u0141apa, K., Przyby\u0142, A., Cpa\u0142ka, K.: A new approach to designing interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7895, pp. 523\u2013534. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38610-7_48"},{"key":"2_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/978-3-642-38658-9_30","volume-title":"Artificial Intelligence and Soft Computing","author":"K \u0141apa","year":"2013","unstructured":"\u0141apa, K., Zalasi\u0144ski, M., Cpa\u0142ka, K.: A new method for designing and complexity reduction of neuro-fuzzy systems for nonlinear modelling. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7894, pp. 329\u2013344. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38658-9_30"},{"key":"2_CR32","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1137\/0111030","volume":"11","author":"D Marqardt","year":"1963","unstructured":"Marqardt, D.: An algorithm for last-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431\u2013441 (1963)","journal-title":"J. Soc. Ind. Appl. Math."},{"issue":"2","key":"2_CR33","doi-asserted-by":"publisher","first-page":"143","DOI":"10.2478\/jaiscr-2021-0009","volume":"11","author":"T Niksa-Rynkiewicz","year":"2021","unstructured":"Niksa-Rynkiewicz, T., Szewczuk-Krypa, N., Witkowska, A., Cpa\u0142ka, K., Zalasi\u0144ski, M., Cader, A.: Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network. J. Artif. Intell. Soft Comput. Res. 11(2), 143\u2013155 (2021). https:\/\/doi.org\/10.2478\/jaiscr-2021-0009","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"2","key":"2_CR34","first-page":"103","volume":"1","author":"K Patan","year":"2011","unstructured":"Patan, K., Patan, M.: Optimal training strategies for locally recurrent neural networks. J. Artif. Intell. Soft Comput. Res. 1(2), 103\u2013114 (2011)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"2_CR35","unstructured":"Riedmiller, M., Braun, H.: A direct method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks, San Francisco (1993)"},{"issue":"3","key":"2_CR36","doi-asserted-by":"publisher","first-page":"277","DOI":"10.2478\/jaiscr-2014-0020","volume":"3","author":"M Romaszewski","year":"2013","unstructured":"Romaszewski, M., Gawron, P., Opozda, S.: Dimensionality reduction of dynamic mesh animations using HO-SVD. J. Artif. Intell. Soft Comput. Res. 3(3), 277\u2013289 (2013)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"2_CR37","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McCelland, J. (eds.) Parallel Distributed Processing, vol. 1, Chapter 8. The MIT Press, Cambridge, Massachusetts, (1986)"},{"issue":"10","key":"2_CR38","doi-asserted-by":"publisher","first-page":"3062","DOI":"10.1109\/78.277809","volume":"41","author":"L Rutkowski","year":"1993","unstructured":"Rutkowski, L.: Multiple Fourier series procedures for extraction of nonlinear regressions from noisy data. IEEE Trans. Signal Process. 41(10), 3062\u20133065 (1993)","journal-title":"IEEE Trans. Signal Process."},{"issue":"1","key":"2_CR39","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1109\/18.61144","volume":"37","author":"L Rutkowski","year":"1991","unstructured":"Rutkowski, L.: Identification of MISO nonlinear regressions in the presence of a wide class of disturbances. IEEE Trans. Inf. Theory 37(1), 214\u2013216 (1991)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"1","key":"2_CR40","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/TKDE.2013.34","volume":"26","author":"L Rutkowski","year":"2014","unstructured":"Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108\u2013119 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2_CR41","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1007\/978-3-642-13232-2_79","volume-title":"Artificial Intelligence and Soft Computing","author":"L Rutkowski","year":"2010","unstructured":"Rutkowski, L., Przyby\u0142, A., Cpa\u0142ka, K., Er, M.J.: Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6114, pp. 645\u2013650. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13232-2_79"},{"issue":"10","key":"2_CR42","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1109\/9.35283","volume":"34","author":"L Rutkowski","year":"1989","unstructured":"Rutkowski, L., Rafajlowicz, E.: On optimal global rate of convergence of some nonparametric identification procedures. IEEE Trans. Autom. Control 34(10), 1089\u20131091 (1989)","journal-title":"IEEE Trans. Autom. Control"},{"key":"2_CR43","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/978-3-030-36808-1_78","volume-title":"Neural Information Processing","author":"T Rutkowski","year":"2019","unstructured":"Rutkowski, T., \u0141apa, K., Jaworski, M., Nielek, R., Rutkowska, D.: On explainable flexible fuzzy recommender and its performance evaluation using the Akaike information criterion. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. CCIS, vol. 1142, pp. 717\u2013724. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-36808-1_78"},{"key":"2_CR44","unstructured":"Smola\u0327g, J., Bilski, J.: A systolic array for fast learning of neural networks. In: Proceedings of V Conference on Neural Networks and Soft Computing, Zakopane, pp. 754\u2013758 (2000)"},{"key":"2_CR45","unstructured":"Smola\u0327g, J., Rutkowski, L., Bilski, J.: Systolic array for neural networks. In: Proceedings of IV Conference on Neural Networks and Their Applications, Zakopane, pp. 487\u2013497 (1999)"},{"key":"2_CR46","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1007\/978-3-642-38610-7_23","volume-title":"Artificial Intelligence and Soft Computing","author":"A Starczewski","year":"2013","unstructured":"Starczewski, A.: A clustering method based on the modified RS validity index. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7895, pp. 242\u2013250. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38610-7_23"},{"key":"2_CR47","doi-asserted-by":"publisher","unstructured":"Starczewski, J.T.: Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty, vol. 284. Studies in Fuzziness and Soft Computing. Springer, Cham (2013). https:\/\/doi.org\/10.1007\/978-3-642-29520-1","DOI":"10.1007\/978-3-642-29520-1"},{"issue":"4","key":"2_CR48","doi-asserted-by":"publisher","first-page":"271","DOI":"10.2478\/jaiscr-2020-0018","volume":"10","author":"JT Starczewski","year":"2020","unstructured":"Starczewski, J.T., Goetzen, P., Napoli, Ch.: Triangular fuzzy-rough set based fuzzification of fuzzy rule-based systems. J. Artif. Intell. Soft Comput. Res. 10(4), 271\u2013285 (2020)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"2_CR49","unstructured":"Tadeusiewicz, R.: Neural Networks. AOW RM (1993). (in Polish)"},{"key":"2_CR50","doi-asserted-by":"crossref","unstructured":"Werbos, J.: Backpropagation through time: what it does and how to do it. In: Proceedings of the IEEE, vol. 78, no. 10 (1990)","DOI":"10.1109\/5.58337"},{"issue":"11","key":"2_CR51","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1109\/TNN.2010.2073482","volume":"21","author":"BM Wilamowski","year":"2010","unstructured":"Wilamowski, B.M., Yo, H.: Neural network learning without backpropagation. IEEE Trans. Neural Networks 21(11), 1793\u20131803 (2010)","journal-title":"IEEE Trans. Neural Networks"},{"key":"2_CR52","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/978-3-642-38610-7_32","volume-title":"Artificial Intelligence and Soft Computing","author":"M Zalasi\u0144ski","year":"2013","unstructured":"Zalasi\u0144ski, M., Cpa\u0142ka, K.: New approach for the on-line signature verification based on method of horizontal partitioning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7895, pp. 342\u2013350. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38610-7_32"},{"key":"2_CR53","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.eswa.2018.03.028","volume":"104","author":"M Zalasi\u0144ski","year":"2018","unstructured":"Zalasi\u0144ski, M., \u0141apa, K., Cpa\u0142ka, K.: Prediction of values of the dynamic signature features. Expert Syst. Appl. 104, 86\u201396 (2018)","journal-title":"Expert Syst. Appl."},{"key":"2_CR54","doi-asserted-by":"publisher","unstructured":"El Zini, J., Rizk, Y., Awad, M.: An optimized parallel implementation of non-iteratively trained recurrent neural networks. J. Artif. Intell. Soft Comput. Res. 11(1), 33\u201350 (2021). https:\/\/doi.org\/10.2478\/jaiscr-2021-0003","DOI":"10.2478\/jaiscr-2021-0003"},{"issue":"1","key":"2_CR55","doi-asserted-by":"publisher","first-page":"33","DOI":"10.53106\/160792642022012301004","volume":"23","author":"Z Sun","year":"2022","unstructured":"Sun, Z., Zhao, Z., Scherer, R., Wei, W., Wo\u017aniak, M.: An overview of capsule neural networks. J. Internet Technol. 23(1), 33\u201344 (2022)","journal-title":"J. Internet Technol."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42505-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:33:24Z","timestamp":1730075604000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42505-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031425042","9783031425059"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42505-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAISC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Soft Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zakopane","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaisc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icaisc.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Custom","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"175","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":"84","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":"0","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":"48% - 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","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":"2","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}