Application of BP Neural Networks on the Thickness Prediction of Sherardizing Coating

Long, Junbi; Li, X. B.; Zhong, Y.; Dian, Peng

doi:10.1007/s12666-019-01696-y

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…of stock price data. Moreover, previous studies have shown that BPNN model has disadvantages such as slow convergence speed, large randomness of weight and threshold adjustment process [19]. Therefore, when interference occurs, BPNN model can't solve the problem of poor anti-interference by itself.…”

Section: Plos Onementioning

confidence: 99%

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects

Zhong

Xiao-ling

2020

PLoS ONE

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To determine the influence of the weight of the economic effectiveness evaluation criteria of the major investments of listed enterprises, and provide new management ideas for the development of the follow-up enterprises, firstly, the financial benefit evaluation system of investment projects is analyzed and constructed, and the specific evaluation process is analyzed. Then, on this basis, the evaluation index is refined; the basic structure of BP neural network (BPNN) is introduced, and genetic algorithm is used to improve BP neural network. The cost-benefit analysis model is constructed based on the improved BPNN. The listed company A is taken as an example to analyze its development data in recent years, and then the data of 10 listed companies are taken as the research object. Matlab simulation software is used to train and verify the improved BPNN model, analyze and predict the weight value of the financial benefit index of the investment projects of these 10 companies, and then determine the index to improve the financial benefit of the investment projects. Under the analysis of the development data of listed company A in the past 10 years, it is found that the indicators of the listed company's profitability per share, debt risk operation ability, development, and growth ability in the past 10 years are in relatively stable state. The principal component analysis of its 20 secondary sub-indexes is conducted based on the four primary indicators: profitability, debt risk, operational capacity, and development and growth. A total of eight principal components including return on equity (ROE), return on assets (ROA), (total asset turnover) TATO, turnover of account receivable (AR), asset-liability ratio, interest protection multiple, income growth rate, and year-on-year rate of increase for complete assets are extracted. The average error between the final output value, the actual value, and the expected value is 0.0304 and 0.0169, respectively. The weight coefficient of the monetary benefits evaluation indicator of investment items is calculated, and the computed results show that year-on-year rate of increase for complete assets, TOTA, ROA, turnover of total capital, and ROE are important indexes in the financial benefit evaluation of investment projects. It indicates that to improve the financial benefit of investment projects of listed enterprises, it is necessary to enhance the year-over-year growth degree of total properties and ROA.

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Section: Plos Onementioning

confidence: 99%

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects

Zhong

Xiao-ling

2020

PLoS ONE

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“…ANNs, an example of the most powerful algorithms of machine learning models, have been widely applied for metal sciences and atmospheric corrosion fields due to their advantages [30][31][32][33][34]. ose typical benefits are as follows: (1) ANN is a nonlinear model, which is easy to use and understand compared to statistical methods, and (2) ANNs allow the modeling of physical phenomena in complex systems without requiring explicit mathematical representations.…”

Section: Introductionmentioning

confidence: 99%

“…eir findings showed that neural network models had a reliable prediction with a small error and a large coefficient of determination value (R 2 ). e ANN models were effective for various investigations such as thickness prediction of sherardizing coating [31], corrosion of metals in equatorial climate [33], and corrosion of copper in Valparaiso (Chile) [34]. Particularly, ANN was used for predicting the penetration of corrosion or the corrosion rate of carbon steel considering input parameters such as humidity, temperature, time of wetness, precipitation, sulfur dioxide concentration (SO 2 ), and chloride deposition rate (Cl − ) [30,35].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning‐Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel

Tran

Nguyen

Phan

et al. 2021

Advances in Materials Science and Engineering

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The purpose of this study is to develop a practical artificial neural network (ANN) model for predicting the atmospheric corrosion rate of carbon steel. A set of 240 data samples, which are collected from the experimental results of atmospheric corrosion in tropical climate conditions, are utilized to develop the ANN model. Accordingly, seven meteorological and chemical factors of corrosion, namely, the average temperature, the average relative humidity, the total rainfall, the time of wetness, the hours of sunshine, the average chloride ion concentration, and the average sulfur dioxide deposition rate, are used as input variables for the ANN model. Meanwhile, the atmospheric corrosion rate of carbon steel is considered as the output variable. An optimal ANN model with a high coefficient of determination of 0.999 and a small root mean square error of 0.281 mg/m2.month is retained to predict the corrosion rate. Moreover, the sensitivity analysis shows that the rainfall and hours of sunshine are the most influential parameters on predicting the atmospheric corrosion rate, whereas the average chloride ion concentration, the average temperature, and the time of wetness are less sensitive to the atmospheric corrosion rate. An ANN-based formula, which accommodates all input parameters, is thereafter proposed to estimate the atmospheric corrosion rate of carbon steel. Finally, a graphical user interface is developed for calculating the atmospheric corrosion rate of carbon steel in tropical climate conditions.

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The Role of Machine Learning in Tribology: A Systematic Review

Paturi¹,

Palakurthy²,

Reddy

2022

Arch Computat Methods Eng

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Application of BP Neural Networks on the Thickness Prediction of Sherardizing Coating

Cited by 4 publications

References 16 publications

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects

A Machine Learning‐Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel

The Role of Machine Learning in Tribology: A Systematic Review

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