Please use this identifier to cite or link to this item: https://doi.org/10.1016/0893-6080(88)90354-1
DC FieldValue
dc.titleError tolerant environment of multilayer perceptrons with controlled learning
dc.contributor.authorYu, Wellington C.P.
dc.contributor.authorTeh, Hoon-Heng
dc.date.accessioned2014-10-28T02:34:32Z
dc.date.available2014-10-28T02:34:32Z
dc.date.issued1988
dc.identifier.citationYu, Wellington C.P.,Teh, Hoon-Heng (1988). Error tolerant environment of multilayer perceptrons with controlled learning. Neural Networks 1 (1 SUPPL) : 323-. ScholarBank@NUS Repository. <a href="https://doi.org/10.1016/0893-6080(88)90354-1" target="_blank">https://doi.org/10.1016/0893-6080(88)90354-1</a>
dc.identifier.issn08936080
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/103208
dc.description.abstractThe back-propagation learning algorithm of multilayer perceptrons has provided a way to determine the weight-matrices. However, besides giving a learned solution, the method does not reveal the structure of the multilayer perceptrons, the number of layers, and the number of nodes, for performing further analysis. To solve the shortcomings of back-propagation learning, we have developed an algorithm to obtain quickly an exact upper-bound solution for a 2-layer perceptron for a given n input patterns of size m, and n output patterns of size k. For reduction of nodes in the hidden unit, we have developed an algorithm to minimize the hidden layer structure for an exact lower-bound solution. To cope with the problem of not error tolerant, we have developed an algorithm to modify the weight and threshold functions so that inputs with errors can be tolerated.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/0893-6080(88)90354-1
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentMATHEMATICS
dc.description.doi10.1016/0893-6080(88)90354-1
dc.description.sourcetitleNeural Networks
dc.description.volume1
dc.description.issue1 SUPPL
dc.description.page323-
dc.description.codenNNETE
dc.identifier.isiutNOT_IN_WOS
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