Phyu Hnin Thike1, 2, Zhaoyang Zhao1, Peng Liu1, Feihu Bao1, Ying Jin1, Peng Shi1, *
CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2091-2109, 2020, DOI:10.32604/cmc.2020.011608
Abstract The optimization of network topologies to retain the generalization ability by
deciding when to stop overtraining an artificial neural network (ANN) is an existing vital
challenge in ANN prediction works. The larger the dataset the ANN is trained with, the
better generalization the prediction can give. In this paper, a large dataset of atmospheric
corrosion data of carbon steel compiled from several resources is used to train and test a
multilayer backpropagation ANN model as well as two conventional corrosion prediction
models (linear and Klinesmith models). Unlike previous related works, a grid searchbased hyperparameter tuning is performed to develop multiple… More >
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