Feedforward neural network with multi-valued connection weights

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dc.contributor.authorThammano, Arit
dc.contributor.authorRuxpakawong, Phongthep
dc.date.accessioned2026-08-06T09:58:38Z
dc.date.available2026-08-06T09:58:38Z
dc.date.issued2009-09-11
dc.description.abstractThis paper introduces a new concept of the connection weight to the multi-layer feedforward neural network. The architecture of the proposed approach is the same as that of the original multi-layer feedforward neural network. However, the weight of each connection is multi-valued, depending on the value of the input data involved. The backpropagation learning algorithm was also modified to suit the proposed concept. This proposed model has been benchmarked against the original feedforward neural network and the radial basis function network. The results on six benchmark problems are very encouraging. © 2009 Springer Berlin Heidelberg.
dc.identifier.citationLecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5551 LNCS(PART 1), 229-237, 2009
dc.identifier.doi10.1007/978-3-642-01507-6_27
dc.identifier.issn03029743
dc.identifier.other2-s2.0-69949083684
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/2587
dc.sourceLecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
dc.subjectClassification
dc.subjectData mining
dc.subjectFeedforward Neural Network
dc.subjectLearning algorithm
dc.titleFeedforward neural network with multi-valued connection weights
dc.typeConference Paper

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