Backpropagation Neural Network with Adaptive Learning Rate for Classification

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dc.contributor.authorJullapak, Rujira
dc.contributor.authorThammano, Arit
dc.date.accessioned2026-08-06T10:40:28Z
dc.date.available2026-08-06T10:40:28Z
dc.date.issued2023-01-01
dc.description.abstractThis research aims to improve the classification accuracy by modifying an original backpropagation neural network. In the proposed BPNN-ZMP, the learning rates were automatic tuned to improve the classification accuracy. Breast Cancer Coimbra dataset and Banknote Authentication dataset were used for testing the model performances. The results demonstrate that BPNN-ZMP improved over the original backpropagation neural network by 12.12 and 11.46% for Breast Cancer Coimbra dataset and Banknote Authentication dataset respectively. Although BPNN-ZMP could improve the model accuracy, the high accuracy in neural network backpropagation has been challenged in future work.
dc.identifier.citationLecture Notes on Data Engineering and Communications Technologies, 153, 493-499, 2023
dc.identifier.doi10.1007/978-3-031-20738-9_56
dc.identifier.issn23674512
dc.identifier.other2-s2.0-85147854524
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14151
dc.sourceLecture Notes on Data Engineering and Communications Technologies
dc.subjectAdaptive learning rate
dc.subjectBackpropagation neural network
dc.subjectClassification
dc.titleBackpropagation Neural Network with Adaptive Learning Rate for Classification
dc.typeBook Chapter

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