Matches in SemOpenAlex for { <https://semopenalex.org/work/W4240432055> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W4240432055 endingPage "2109" @default.
- W4240432055 startingPage "2091" @default.
- W4240432055 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 hyperparameter combinations (network topologies) to train multiple ANNs with mini-batch stochastic gradient descent optimization algorithm to facilitate the training of a large dataset. After that, one selection strategy for the optimal hyperparameter combination is applied by an early stopping method to guarantee the generalization ability of the optimal network model. The correlation coefficients (R) of the ANN model can explain about 80% (more than 75%) of the variance of atmospheric corrosion of carbon steel, and the root mean square errors (RMSE) of three models show that the ANN model gives a better performance than the other two models with acceptable generalization. The influence of input parameters on the output is highlighted by using the fuzzy curve analysis method. The result reveals that TOW, Cl- and SO2 are the most important atmospheric chemical variables, which have a well-known nonlinear relationship with atmospheric corrosion." @default.
- W4240432055 created "2022-05-12" @default.
- W4240432055 creator A5016404871 @default.
- W4240432055 creator A5018814025 @default.
- W4240432055 creator A5055551312 @default.
- W4240432055 creator A5068626745 @default.
- W4240432055 creator A5081472985 @default.
- W4240432055 creator A5087128953 @default.
- W4240432055 date "2020-01-01" @default.
- W4240432055 modified "2023-09-25" @default.
- W4240432055 title "An Early Stopping-Based Artificial Neural Network Model for Atmospheric Corrosion Prediction of Carbon Steel" @default.
- W4240432055 doi "https://doi.org/10.32604/cmc.2020.011608" @default.
- W4240432055 hasPublicationYear "2020" @default.
- W4240432055 type Work @default.
- W4240432055 citedByCount "7" @default.
- W4240432055 countsByYear W42404320552021 @default.
- W4240432055 countsByYear W42404320552022 @default.
- W4240432055 countsByYear W42404320552023 @default.
- W4240432055 crossrefType "journal-article" @default.
- W4240432055 hasAuthorship W4240432055A5016404871 @default.
- W4240432055 hasAuthorship W4240432055A5018814025 @default.
- W4240432055 hasAuthorship W4240432055A5055551312 @default.
- W4240432055 hasAuthorship W4240432055A5068626745 @default.
- W4240432055 hasAuthorship W4240432055A5081472985 @default.
- W4240432055 hasAuthorship W4240432055A5087128953 @default.
- W4240432055 hasBestOaLocation W42404320551 @default.
- W4240432055 hasConcept C10485038 @default.
- W4240432055 hasConcept C105795698 @default.
- W4240432055 hasConcept C119857082 @default.
- W4240432055 hasConcept C12267149 @default.
- W4240432055 hasConcept C134306372 @default.
- W4240432055 hasConcept C139945424 @default.
- W4240432055 hasConcept C154945302 @default.
- W4240432055 hasConcept C155032097 @default.
- W4240432055 hasConcept C177148314 @default.
- W4240432055 hasConcept C33923547 @default.
- W4240432055 hasConcept C41008148 @default.
- W4240432055 hasConcept C50644808 @default.
- W4240432055 hasConcept C5465570 @default.
- W4240432055 hasConcept C8642999 @default.
- W4240432055 hasConceptScore W4240432055C10485038 @default.
- W4240432055 hasConceptScore W4240432055C105795698 @default.
- W4240432055 hasConceptScore W4240432055C119857082 @default.
- W4240432055 hasConceptScore W4240432055C12267149 @default.
- W4240432055 hasConceptScore W4240432055C134306372 @default.
- W4240432055 hasConceptScore W4240432055C139945424 @default.
- W4240432055 hasConceptScore W4240432055C154945302 @default.
- W4240432055 hasConceptScore W4240432055C155032097 @default.
- W4240432055 hasConceptScore W4240432055C177148314 @default.
- W4240432055 hasConceptScore W4240432055C33923547 @default.
- W4240432055 hasConceptScore W4240432055C41008148 @default.
- W4240432055 hasConceptScore W4240432055C50644808 @default.
- W4240432055 hasConceptScore W4240432055C5465570 @default.
- W4240432055 hasConceptScore W4240432055C8642999 @default.
- W4240432055 hasIssue "3" @default.
- W4240432055 hasLocation W42404320551 @default.
- W4240432055 hasOpenAccess W4240432055 @default.
- W4240432055 hasPrimaryLocation W42404320551 @default.
- W4240432055 hasRelatedWork W2804162248 @default.
- W4240432055 hasRelatedWork W3199608561 @default.
- W4240432055 hasRelatedWork W4280535922 @default.
- W4240432055 hasRelatedWork W4281646320 @default.
- W4240432055 hasRelatedWork W4283697347 @default.
- W4240432055 hasRelatedWork W4294564511 @default.
- W4240432055 hasRelatedWork W4295309597 @default.
- W4240432055 hasRelatedWork W4298144215 @default.
- W4240432055 hasRelatedWork W4313854490 @default.
- W4240432055 hasRelatedWork W4322722559 @default.
- W4240432055 hasVolume "65" @default.
- W4240432055 isParatext "false" @default.
- W4240432055 isRetracted "false" @default.
- W4240432055 workType "article" @default.