[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

Liu, Tianzeng; Guang-ping, Zhang

doi:10.1155/2021/2115653

Cited by 10 publications

(9 citation statements)

References 24 publications

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“…A backward propagation neural network (BPNN) is a multilayer feedforward neural network with output results using forward propagation and errors using backward propagation [ 29 ]. The main working principle of the BPNN is to use machine learning to continuously iterate the training model, calculate the error between the actual and expected output values based on the minimum mean squared error criterion, and adjust the weights and thresholds of each layer of the network using the gradient descent strategy to minimize the error [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China

Zhao¹,

Zhang²,

Zhang

et al. 2023

BMC Public Health

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Background This study aimed to construct a more accurate model to forecast the incidence of hand, foot, and mouth disease (HFMD) in mainland China from January 2008 to December 2019 and to provide a reference for the surveillance and early warning of HFMD. Methods We collected data on the incidence of HFMD in mainland China between January 2008 and December 2019. The SARIMA, SARIMA-BPNN, and SARIMA-PSO-BPNN hybrid models were used to predict the incidence of HFMD. The prediction performance was compared using the mean absolute error(MAE), mean squared error(MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation analysis. Results The incidence of HFMD in mainland China from January 2008 to December 2019 showed fluctuating downward trends with clear seasonality and periodicity. The optimal SARIMA model was SARIMA(1,0,1)(2,1,2)[12], with Akaike information criterion (AIC) and Bayesian Schwarz information criterion (BIC) values of this model were 638.72, 661.02, respectively. The optimal SARIMA-BPNN hybrid model was a 3-layer BPNN neural network with nodes of 1, 10, and 1 in the input, hidden, and output layers, and the R-squared, MAE, and RMSE values were 0.78, 3.30, and 4.15, respectively. For the optimal SARIMA-PSO-BPNN hybrid model, the number of particles is 10, the acceleration coefficients c1 and c2 are both 1, the inertia weight is 1, the probability of change is 0.95, and the values of R-squared, MAE, and RMSE are 0.86, 2.89, and 3.57, respectively. Conclusions Compared with the SARIMA and SARIMA-BPNN hybrid models, the SARIMA-PSO-BPNN model can effectively forecast the change in observed HFMD incidence, which can serve as a reference for the prevention and control of HFMD.

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Section: Methodsmentioning

confidence: 99%

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China

Zhao¹,

Zhang²,

Zhang

et al. 2023

BMC Public Health

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“…This algorithm is a well-known algorithm that has been used in many VRP and PDP studies [4], [20], [24]. As a population-based optimization algorithm which adopts evolution mechanism, its process consists of reproduction, cross-over, and mutation [25]- [28]. After the clustering process, the second step is routing.…”

Section: Methodsmentioning

confidence: 99%

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance

Kusuma

Kallista

2022

Bulletin EEI

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One problem in collaborative pickup delivery problem (PDP) was excessive outsourced jobs. It happened in many studies on the collaborative PDP. Besides, the revenue sharing in it was unclear although important. This work aimed to propose a novel collaborative PDP model which minimizes total travel distance while maintains low outsourced jobs. It proposed several contributions. First, it prioritized internal jobs first rather than full collaborative model. Second, it proposed new revenue sharing model. It adopted cluster-first route-second and mixed pickup and delivery. It was developed by combining the genetic algorithm and nearest distance algorithm where the genetic algorithm was used in the clustering process and the nearest distance was used in the routing process. The simulation result shows that the proposed model was better than the comparing models: (1) combined K-means and genetic algorithm model (KMGA) and (2) combined simulated annealing and last-in first-out (SNLIFO) model. When the number of orders was high (300 units), the total travel distance of the proposed model was 37 percent lower than the KMGA model and 30 percent lower than the SNLIFO model. In average, the outsourcing rate of the proposed model was 70 percent lower than the previous models.

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“…After BP neural network training, the test error was obtained, the optimal individual was selected and assigned to the fitness function. The optimal initial weight and threshold in the prediction model was obtained, and the GA-BP neural network model with two overlapping algorithms was constructed [33].…”

Section: Establish Ga-bp Neural Network Modelmentioning

confidence: 99%

Surface quality optimization of laser cladding based on surface response and genetic neural network model

Zhang¹,

Xu²,

Sun³

et al. 2022

Surf. Topogr.: Metrol. Prop.

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The surface roughness is a main performance parameter to characterize the quality of the macroscopic morphology in laser cladding multi-lap forming of key parts. In this paper, both the Taguchi and the Box-Behnken Design (BBD) experiments were used to conduct the laser cladding multi-channel lap experiment. The relationship between the process parameters regarding laser power, powder feeding speed, scanning speed, lap rate and the surface roughness of the coating was illustrated via experiments. The neural network model (Genetic Algorithm-Back Propagation, GA-BP) and response surface methodology (RSM) optimized by genetic algorithm were established, respectively. Furthermore, the optimal cladding parameters were optimized, and the coupling effect between the cladding process and the forming quality of the multi-channel lap was revealed. It is found that the root mean square error (RMSE) and absolute average deviation (AAD) of GA-BP model were both smaller than those of the RSM model. The coefficient of determination R-Squared of GA-BP model was closer to 1. Moreover, the minimum roughness of 20.89 μm by optimizing the process parameters through the genetic neural network model was lower than the surface response optimization of 35.67μm. The genetic neural network model optimized by genetic algorithm can improve the prediction accuracy on the surface macro-quality of multi-channel lap cladding layer, which provides a theoretical basis and technological direction for the improvement of the surface quality of key parts.

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[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

Cited by 10 publications

References 24 publications

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance

Surface quality optimization of laser cladding based on surface response and genetic neural network model

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