Modeling the SOFC by BP neural network algorithm

Song, Shaohui; Xiong, Xingyu; Wu, Xin; Xue, Zhenzhong

doi:10.1016/j.ijhydene.2021.03.132

Cited by 135 publications

(31 citation statements)

References 15 publications

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“…In Song et al's study on forest data recognition, the way to improve the accuracy of BP neural network recognition and classification is discussed. e clustering algorithm is introduced to optimize the BP neural network, which makes up for the slow convergence of the BP neural network algorithm [7].…”

Section: Related Workmentioning

confidence: 99%

An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning Algorithm

Feng

Liu

2022

Scientific Programming

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Load training is an important part of the daily training of aerobics athletes. Therefore, the research on the characteristics of aerobics movement of athletes in load training has been paid extensive attention by teaching units. Deep learning algorithm in artificial intelligence algorithm is used to study the characteristics of calisthenics teaching and training, which is a beneficial exploration to improve the scientific of calisthenics training programs. The basic principles of the neural network algorithm and implementation process are described. After the basic structure and characteristics of the network are analyzed, a comprehensive solution to optimize and improve the regularized deep belief network algorithm is proposed. The results show that compared with other learning algorithms, the feature classification error rate of the optimized regularized deep belief network algorithm is 6% lower than that of other algorithms. Although the training speed of the model decreases, the convergence period of the deep neural network algorithm can better achieve the extraction accuracy of abstract features of training data when the deep learning period increases. Being less affected by input parameters, the algorithm will have better stability and be more conducive to extracting the features favorable to classification. In the field of deep learning, the advantages of the deep neural networks can effectively classify the training characteristics of aerobics athletes and provide scientific decision-making basis for subsequent teaching and training.

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Section: Related Workmentioning

confidence: 99%

An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning Algorithm

Feng

Liu

2022

Scientific Programming

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“…The BP neural network algorithm is based on the method of biological neuron information exchange, simplification, and simulation. The basis of the entire algorithm network is the information processing unit-neuron [22], as shown in Figure 1. The neuron is a multi-input single-output non-linear processing unit, which weights the signal transmitted by the upper level, and performs summation under the action of the excitation function [22]:…”

Section: Bp Neuron Structurementioning

confidence: 99%

Prediction of Abrasive Belt Wear Based on BP Neural Network

Cao

Zhao

et al. 2021

Machines

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Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status of abrasive belt in real-time during the grinding process, and the challenge of time-consuming control while shutdown for detection, this paper proposes a method for predicating the wear of abrasive belt while the grinding process based on back-propagation (BP) neural network. First, experiments are carried out based on ultra-depth-of-field detection technology, and different parameter combinations are used to measure the degree of abrasive belt wear. Then the effects of different grinding speeds, different contact pressures, and different work piece materials on the abrasive belt wear rate are obtained. It can be concluded that the abrasive belt wear rate gradually increases as the grinding speed of the abrasive belt increases. With the increase of steel grade, the hardness of the steel structure increases, which intensifies the abrasive belt wear. As the contact pressure increases, the pressure on a single abrasive particle increases, which ultimately leads to increased wear. With the increase of contact pressure, the increase of the wear rate of materials with higher hardness is greater. By utilizing the artificial intelligence BP neural network method, 18 sets of experiment data are used for training BP neural network while 9 sets of data are used for verification, and the nonlinear mapping relationship between various process parameter combinations such as grinding speed, contact pressure, workpiece material, and wear rate is established to predict the wear degree of abrasive belt. Finally, the results of verification by examples show that the method proposed in this paper can fulfill the purpose of quickly and accurately predicting the degree of abrasive belt wear, which can be used for guiding the manufacturing processing, and greatly improving the processing efficiency.

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“…In order to verify the prediction accuracy of the proposed method, the prediction results are compared of open literature methods based on backpropagation (BP), support vector machine (SVM), and random forest (RF) [35], as shown in Table 4. From the results shown, the average criteria of the proposed methods (learning phase and inference phase) are lower than other existing methods.…”

Section: Comparison Of Performance Degradation Predictionmentioning

confidence: 99%

“…Although the data lengths are different, the comparison results can still demonstrate the effectiveness of the proposed DNN models based on NNARX, NNARMAX, and NNOE. [35] 0.0032 0.0769 Support vector machine (SVM) [35] 0.4492 0.2998 Random forest (RF) [35] 0.524 0.3598…”

Section: Comparison Of Performance Degradation Predictionmentioning

confidence: 99%

Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models

Cheng

Chang

et al. 2021

Energies

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Prognostics technology is important for the sustainability of solid oxide fuel cell (SOFC) system commercialization, i.e., through failure prevention, reliability assessment, and the remaining useful life (RUL) estimation. To solve SOFC system issues, data-driven prognostics methods based on the dynamic neural network (DNN), one of non-linear models, were investigated in this study. Based on DNN model types, the neural network autoregressive (NNARX) model with external inputs, the neural network autoregressive moving average (NNARMAX) model with external inputs, and the neural network output error (NNOE) were utilized to predict the degradation trend and estimate the RUL. First, the degradation trend prediction was executed to evaluate the correctness of the proposed DNN model structures in the first learning phase. Then, the RUL was estimated on the basis of the degradation trend of the NN models in the second inference phase. The comparison test results show the prediction accuracy of the NNARX model is higher and the RUL estimation can be given within a smaller relative error than the NNARMAX and NNOE models. The evaluation criteria of the root mean square error and mean absolute error of the NNARX model are the smallest among these three models. Therefore, the proposed NNARX model can effectively and precisely provide degradation trend prediction and RUL estimation of the SOFC system.

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Modeling the SOFC by BP neural network algorithm

Cited by 135 publications

References 15 publications

An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning Algorithm

An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning Algorithm

Prediction of Abrasive Belt Wear Based on BP Neural Network

Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models

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