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An early stopping-Based Artificial Neural Network Model for Atmospheric Corrosion Prediction of Carbon Steel

Computers, Materials & Continua 2020年第12期65卷 2091-2109页

作者： Phyu Hnin Thike Zhaoyang Zhao Peng Liu Feihu Bao Ying Jin Peng Shi National Center for Materials Service Safety University of Science and Technology BeijingBeijing100083China Department of Computer Engineering and Information Technology Yangon Technological UniversityYangon11181Myanmar

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 *** larger the dataset the ANN is trained with,the better generalization the prediction can *** 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 search-based 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 *** 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 *** 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 *** influence of input parameters on the output is highlighted by using the fuzzy curve analysis *** result reveals that TOW,Cl-and SO2 are the most important atmospheric chemical variables,which have a well-known nonlinear relationship with atmospheric corrosion.

关键词： Atmospheric corrosion prediction early stopping fuzzy curve grid search hyperparameter tuning multilayer neural network

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Probability stopping criterion for analog decoding of LDPC codes

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The Journal of China Universities of Posts and Telecommunications 2017年第1期24卷 35-39页

作者： Zheng Hao Zhang Shuyi Li Lintao Gao Yuan Shao Liwei School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Telecommunication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Research Institute of Beijing Institute of Technology in Zhongshan Zhongshan 528400China

To detect uncorrectable frames and terminate the decoding procedure early, a probability stopping criterion for iterative analog decoding of low density parity check （LDPC） codes is proposed in this paper. By using probabilities of satisfied checks to detect uncorrectable frames and terminate decoding, the proposed criterion could be applied to analog decoders without much structure modifications. Simulation results show that the proposed criterion can reduce the average number of iterations and achieve a better balance in bit error ratio （BER） performance and decoding complexity than other stopping criteria using extrinsic information.

关键词： LDPC codes analog decoding early stopping probability stopping criterion

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New Neural Network Response Surface Methods for Reliability Analysis

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Chinese Journal of Aeronautics 2011年第1期24卷 25-31页

作者： REN Yuan BAI Guangchen School of Jet Propulsion Beijing University of Aeronautics and Astronautics Beijing 100191 China

This article presents two new kinds of artificial neural network （ANN） response surface methods （RSMs）： the ANN RSM based on early stopping technique （ANNRSM-1）, and the ANN RSM based on regularization theory （ANNRSM-2）. The following improvements are made to the conventional ANN RSM （ANNRSM-0）： 1） by monitoring the validation error during the training process, ANNRSM-1 determines the early stopping point and the training stopping point, and the weight vector at the early stopping point, which corresponds to the ANN model with the optimal generalization, is finally returned as the training result; 2） according to the regularization theory, ANNRSM-2 modifies the conventional training performance function by adding to it the sum of squares of the network weights, so the network weights are forced to have smaller values while the training error decreases. Tests show that the performance of ANN RSM becomes much better due to the above-mentioned improvements： first, ANNRSM-1 and ANNRSM-2 approximate to the limit state function （LSF） more accurately than ANNRSM-0; second, the estimated failure probabilities given by ANNRSM-1 and ANNRSM-2 have smaller errors than that obtained by ANNRSM-0; third, compared with ANNRSM-0, ANNRSM-1 and ANNRSM-2 require much fewer data samples to achieve stable failure probability results.

关键词： neural networks response surface reliability analysis early stopping regularization

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A Gradient Iteration Method for Functional Linear Regression in Reproducing Kernel Hilbert Spaces

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Annals of Applied Mathematics 2022年第3期38卷 280-295页

作者： Hongzhi Tong Michael Ng School of Statistics University of International Business and EconomicsBeijing 100029China Institute of Data Science and Department of Mathematics The University of Hong KongPokfulamHong KongChina

We consider a gradient iteration algorithm for prediction of functional linear regression under the framework of reproducing kernel Hilbert *** the algorithm,we use an early stopping technique,instead of the classical Tikhonov regularization,to prevent the iteration from an overfitting *** mild conditions,we obtain upper bounds,essentially matching the known minimax lower bounds,for excess prediction *** almost sure convergence is also established for the proposed algorithm.

关键词： Gradient iteration algorithm functional linear regression reproducing kernel Hilbert space early stopping convergence rates

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