{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:39:26Z","timestamp":1743061166788,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"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_1","type":"book-chapter","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T07:03:11Z","timestamp":1694588591000},"page":"3-11","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Approach to the GQR Algorithm for Neural Networks Training"],"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":"Bartosz","family":"Kowalczyk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"issue":"4","key":"1_CR1","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 feedforwad neural networks. J. Artif. Intell. Soft Comput. Res. 10(4), 299\u2013316 (2020)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"6","key":"1_CR2","first-page":"749","volume":"45","author":"J Bilski","year":"1998","unstructured":"Bilski, J., Rutkowski, L.: A fast training algorithm for neural networks. IEEE Trans. Circ. Syst. Part II 45(6), 749\u2013753 (1998)","journal-title":"IEEE Trans. Circ. Syst. Part II"},{"issue":"9","key":"1_CR3","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1109\/TPDS.2014.2357019","volume":"26","author":"J Bilski","year":"2015","unstructured":"Bilski, J., Smolag, J.: Parallel architectures for learning the RTRN and Elman dynamic neural network. IEEE Trans. Parallel Distrib. Syst. 26(9), 2561\u20132570 (2015)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"1_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-319-59063-9_3","volume-title":"Artificial Intelligence and Soft Computing","author":"J Bilski","year":"2017","unstructured":"Bilski, J., Wilamowski, B.M.: Parallel Levenberg-Marquardt algorithm without error backpropagation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 25\u201339. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59063-9_3"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Bilski, J., Kowalczyk, B., Kisiel-Dorohinicki, M., Siwocha, A., \u017burada, J.: Towards a very fast feedforward multilayer neural networks training algorithm (2022)","DOI":"10.2478\/jaiscr-2022-0012"},{"issue":"4","key":"1_CR6","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., Marjanski, A., Gandor, M., \u017burada, J.: A novel fast feedforward neural networks training algorithm. J. Artif. Intell. Soft Comput. Res. 11(4), 287\u2013306 (2021)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.ins.2020.10.025","volume":"553","author":"J Bilski","year":"2021","unstructured":"Bilski, J., Rutkowski, L., Smol\u0105g, J., Tao, D.: A novel method for speed training acceleration of recurrent neural networks. Inf. Sci. 553, 266\u2013279 (2021)","journal-title":"Inf. Sci."},{"issue":"2","key":"1_CR8","doi-asserted-by":"publisher","first-page":"45","DOI":"10.2478\/jaiscr-2023-0006","volume":"12","author":"J Bilski","year":"2023","unstructured":"Bilski, J., Smol\u0105g, J., Kowalczyk, B., 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)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"2","key":"1_CR9","doi-asserted-by":"publisher","first-page":"111","DOI":"10.2478\/jaiscr-2021-0008","volume":"11","author":"N Bougueroua","year":"2021","unstructured":"Bougueroua, N., Mazouzi, S., Belaoued, M., Seddari, N., Derhab, A., Bouras, A.: A survey on multi-agent based collaborative intrusion detection systems. J. Artif. Intell. Soft Comput. Res. 11(2), 111\u2013142 (2021)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"4","key":"1_CR10","doi-asserted-by":"publisher","first-page":"243","DOI":"10.2478\/jaiscr-2021-0016","volume":"11","author":"R Cierniak","year":"2021","unstructured":"Cierniak, R., et al.: A new statistical reconstruction method for the computed tomography using an x-ray tube with flying focal spot. J. Artif. Intell. Soft Comput. Res. 11(4), 243\u2013266 (2021)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"1_CR11","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121\u20132159 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"1_CR12","doi-asserted-by":"publisher","first-page":"243","DOI":"10.2478\/jaiscr-2020-0016","volume":"10","author":"M Gabryel","year":"2020","unstructured":"Gabryel, M., Grzanek, K., Hayashi, Y.: Browser fingerprint coding methods increasing the effectiveness of user identification in the web traffic. J. Artif. Intell. Soft Comput. Res. 10(4), 243\u2013253 (2020)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"4","key":"1_CR13","doi-asserted-by":"publisher","first-page":"255","DOI":"10.2478\/jaiscr-2022-0017","volume":"12","author":"M Gabryel","year":"2022","unstructured":"Gabryel, M., Lada, D., Filutowicz, Z., Patora-Wysocka, Z., Kisiel-Dorohinicki, M., Chen, G.Y.: Detecting anomalies in advertising web traffic with the use of the variational autoencoder. J. Artif. Intell. Soft Comput. Res. 12(4), 255\u2013256 (2022)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"1_CR14","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, 989\u2013993 (1994)","journal-title":"IEEE Trans. Neural Networks"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Hinton, G., Sejnowski, T.J.: Unsupervised Learning: Foundations of Neural Computation. The MIT Press, Cambridge (1999)","DOI":"10.7551\/mitpress\/7011.001.0001"},{"key":"1_CR16","volume-title":"Numeryczna Algebra Liniowa: Wprowadzenie do Oblicze\u0144 Zautomatyzowanych","author":"A Kie\u0142basi\u0144ski","year":"1992","unstructured":"Kie\u0142basi\u0144ski, A., Schwetlick, H.: Numeryczna Algebra Liniowa: Wprowadzenie do Oblicze\u0144 Zautomatyzowanych. Wydawnictwa Naukowo-Techniczne, Warszawa (1992)"},{"key":"1_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)"},{"issue":"2","key":"1_CR18","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., Cpalka, K., Zalasinski, 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)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"2","key":"1_CR19","doi-asserted-by":"publisher","first-page":"79","DOI":"10.2478\/jaiscr-2022-0006","volume":"12","author":"ME P\u00e9rez-Pons","year":"2022","unstructured":"P\u00e9rez-Pons, M.E., Parra-Dominguez, J., Omatu, S., Herrera-Viedma, E., Corchado, J.M.: Machine learning and traditional econometric models: a systematic mapping study. J. Artif. Intell. Soft Comput. Res. 12(2), 79\u2013100 (2022)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"1_CR20","unstructured":"Werbos, J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Harvard University (1974)"},{"issue":"2","key":"1_CR21","doi-asserted-by":"publisher","first-page":"63","DOI":"10.2478\/jaiscr-2023-0007","volume":"12","author":"P Woldan","year":"2023","unstructured":"Woldan, P., Duda, P., Cader, A., Laktionov, I.: A new approach to image-based recommender systems with the application of heatmaps maps. J. Artif. Intell. Soft Comput. Res. 12(2), 63\u201372 (2023)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Zalasinski, M., et al.: Evolutionary algorithm for selecting dynamic signatures partitioning approach (2022)","DOI":"10.2478\/jaiscr-2022-0018"},{"key":"1_CR23","unstructured":"Zeiler, M.: ADADELTA: an adaptive learning rate method (2012)"},{"issue":"4","key":"1_CR24","doi-asserted-by":"publisher","first-page":"255","DOI":"10.2478\/jaiscr-2020-0017","volume":"10","author":"X Zhao","year":"2020","unstructured":"Zhao, X., Song, M., Liu, A., Wang, Y., Wang, T., Cao, J.: Data-driven temporal-spatial model for the prediction of AQI in Nanjing. J. Artif. Intell. Soft Comput. Res. 10(4), 255\u2013270 (2020)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"1","key":"1_CR25","doi-asserted-by":"publisher","first-page":"33","DOI":"10.2478\/jaiscr-2021-0003","volume":"11","author":"J El Zini","year":"2021","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)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"1_CR26","unstructured":"\u017burada, J.M.: Introduction to Artificial Neural Systems. West (1992)"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"\u0141apa, K., Cpa\u0142ka, K., Kisiel-Dorohinicki, M., Paszkowski, J., D\u0119bski, M., Le, V.-H.: Multi-population-based algorithm with an exchange of training plans based on population evaluation (2022)","DOI":"10.2478\/jaiscr-2022-0016"}],"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_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T07:03:43Z","timestamp":1694588623000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42505-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031425042","9783031425059"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42505-9_1","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)"}}]}}