{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T20:57:08Z","timestamp":1778101028156,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T00:00:00Z","timestamp":1568851200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T00:00:00Z","timestamp":1568851200000},"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":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s12652-019-01488-8","type":"journal-article","created":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T11:03:47Z","timestamp":1568891027000},"page":"15643-15657","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Two noise tolerant incremental learning algorithms for single layer feed-forward neural networks"],"prefix":"10.1007","volume":"14","author":[{"given":"Muideen","family":"Adegoke","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiu Tung","family":"Wong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0962-6723","authenticated-orcid":false,"given":"Andrew Chi Sing","family":"Leung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Sum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,19]]},"reference":[{"issue":"3","key":"1488_CR1","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1109\/18.256500","volume":"39","author":"AR Barron","year":"1993","unstructured":"Barron AR (1993) Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans Inf Theory 39(3):930\u2013945","journal-title":"IEEE Trans Inf Theory"},{"key":"1488_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0960-7","author":"X Bi","year":"2018","unstructured":"Bi X, Ma H, Li J, Ma Y, Chen D (2018) A positive and unlabeled learning framework based on extreme learning machine for drug-drug interactions discovery. J Ambient Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-018-0960-7","journal-title":"J Ambient Intell Human Comput"},{"key":"1488_CR3","unstructured":"Lichman M (2013) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml. Accessed 2019"},{"key":"1488_CR4","first-page":"237","volume":"3","author":"JB Burr","year":"1991","unstructured":"Burr JB (1991) Digital neural network implementations. Neural Netw Concept Appl Implement 3:237\u2013285","journal-title":"Neural Netw Concept Appl Implement"},{"key":"1488_CR5","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neucom.2016.11.003","volume":"224","author":"RB Feng","year":"2017","unstructured":"Feng RB, Han ZF, Wan WY, Leung CS (2017) Properties and learning algorithms for faulty RBF networks with coexistence of weight and node failures. Neurocomputing 224:166\u2013176","journal-title":"Neurocomputing"},{"key":"1488_CR6","unstructured":"Gu\u00e9ly F, Siarry P (1993) Gradient descent method for optimizing various fuzzy rule bases. In: Second IEEE international conference on fuzzy systems, 1993, pp 1241\u20131246"},{"issue":"5","key":"1488_CR7","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359\u2013366","journal-title":"Neural Netw"},{"issue":"2","key":"1488_CR8","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","volume":"4","author":"K Hornik","year":"1991","unstructured":"Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251\u2013257","journal-title":"Neural Netw"},{"issue":"1\u20133","key":"1488_CR9","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"issue":"4","key":"1488_CR10","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/TNN.2006.875977","volume":"17","author":"GB Huang","year":"2006","unstructured":"Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879\u2013892","journal-title":"IEEE Trans Neural Netw"},{"issue":"16\u201318","key":"1488_CR11","doi-asserted-by":"publisher","first-page":"3056","DOI":"10.1016\/j.neucom.2007.02.009","volume":"70","author":"GB Huang","year":"2007","unstructured":"Huang GB, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16\u201318):3056\u20133062","journal-title":"Neurocomputing"},{"issue":"8","key":"1488_CR12","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1109\/TNN.2010.2049580","volume":"21","author":"CS Leung","year":"2010","unstructured":"Leung CS, Wang HJ, Sum J (2010) On the selection of weight decay parameter for faulty networks. IEEE Trans Neural Netw 21(8):1232\u20131244","journal-title":"IEEE Trans Neural Netw"},{"key":"1488_CR13","doi-asserted-by":"publisher","unstructured":"Li Y, Zhang S, Yin Y, Xiao W, Zhang J (2018) Parallel one-class extreme learning machine for imbalance learning based on Bayesian approach. J Ambient Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-018-0994-x","DOI":"10.1007\/s12652-018-0994-x"},{"issue":"10","key":"1488_CR14","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1109\/PROC.1969.7388","volume":"57","author":"B Liu","year":"1969","unstructured":"Liu B, Kaneko T (1969) Error analysis of digital filters realized with floating-point arithmetic. Proc IEEE 57(10):1735\u20131747","journal-title":"Proc IEEE"},{"issue":"8","key":"1488_CR15","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1109\/TNNLS.2012.2199517","volume":"23","author":"HR Mahdiani","year":"2012","unstructured":"Mahdiani HR, Fakhraie SM, Lucas C (2012) Relaxed fault-tolerant hardware implementation of neural networks in the presence of multiple transient errors. IEEE Trans Neural Netw Learn Systems 23(8):1215\u20131228","journal-title":"IEEE Trans Neural Netw Learn Systems"},{"key":"1488_CR16","doi-asserted-by":"crossref","unstructured":"Martolia R, Jain A, Singla L (2015) Analysis & survey on fault tolerance in radial basis function networks. In: 2015 IEEE international conference on computing, communication & automation (ICCCA), pp 469\u2013473","DOI":"10.1109\/CCAA.2015.7148422"},{"key":"1488_CR17","doi-asserted-by":"crossref","unstructured":"Murakami M, Honda N (2007) Fault tolerance comparison of IDS models with multilayer perceptron and radial basis function networks. In: International joint conference on neural networks 2007 (IJCNN2007), pp 1079\u20131084, IEEE","DOI":"10.1109\/IJCNN.2007.4371108"},{"issue":"5","key":"1488_CR18","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1016\/j.neucom.2010.10.007","volume":"74","author":"J Pajarinen","year":"2011","unstructured":"Pajarinen J, Peltonen J, Uusitalo MA (2011) Fault tolerant machine learning for nanoscale cognitive radio. Neurocomputing 74(5):753\u2013764","journal-title":"Neurocomputing"},{"key":"1488_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-017-0639-5","author":"SJ Wang","year":"2017","unstructured":"Wang SJ, Muhammad K, Phillips P, Dong Z, Zhang YD (2017) Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension. J Ambient Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-017-0639-5","journal-title":"J Ambient Intell Human Comput"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-019-01488-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-019-01488-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-019-01488-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T11:22:24Z","timestamp":1703676144000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-019-01488-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,19]]},"references-count":19,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["1488"],"URL":"https:\/\/doi.org\/10.1007\/s12652-019-01488-8","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,19]]},"assertion":[{"value":"30 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}