Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network

Wang, Jiquan; Zhang, Hongyu; Song, Haohao; Zhang, Panli; Bei, Jinling

doi:10.3390/su142416559

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Liu used the BP neural network to establish a tailings pond displacement risk prediction model, and it exhibited a strong nonlinear function approximation ability 10 . Nonlinear fuzzy prediction can be conducted through the BP neural network 11,12,13 , but when the predictions are influenced by multiple factors or the sample set values are highly variable, the calculation is unlikely to converge and instead falls into the local optimum. Therefore, scholars began to attempt to optimize the BP neural network using intelligent algorithms, with good results being reported in chemical energy and other fields 14,15,16 .…”

Section: Introductionmentioning

confidence: 99%

Displacement prediction of fine-grained tailings ponds based on WOA-BP neural network

Liu,

Wang,

Liu

et al. 2024

Preprint

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Tailing reservoir is an important auxiliary facility of mine and a dangerous source of man-made debris flow with high potential energy. China’s tailings ponds are shifting toward fine-grained high dams. Accordingly, displacement is one of the key factors affecting pond stability, and it is important to understand the displacement trend of the tailings pond to ensure its safe operation. Accordingly, this paper adopts the whale algorithm to optimize the back propagation(BP) neural network and establishes the WOA-BP neural network nonlinear prediction model to avoid the error generated by the model experiment due to the scaling effect. The infiltration line and displacement data of a tailings pond in Sichuan Province in the past two years are collected consecutively to form a learning sample, which is then used for training to predict the displacement of the tailings pond through the WOA-BP neural network model. Thereafter, these prediction results are compared with the actual monitoring values as well as the BP neural network model prediction values. The results revealed that the relative error of the WOA-BP neural network model prediction results was approximately 4.5%, and the Pearson correlation coefficients were all above 0.998. Compared with the traditional BP neural network model, the optimization model has a stronger search capability, wider application range, higher prediction accuracy, a more global optimal solution, and better response. The nonlinear fuzzy mapping provides new insights into tailings pond displacement and safety prediction.

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Section: Introductionmentioning

confidence: 99%

Displacement prediction of fine-grained tailings ponds based on WOA-BP neural network

Liu,

Wang,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Since the beginning of the twenty-first century, the use of back-propagation neural networks (BPNNs) to establish prediction models in different fields has been widely utilized in China. While the use of BPNNs to predict ESP efficiency is rare, Wang et al [14] proposed a pork supply prediction method based on an improved mayfly optimization algorithm and back-propagated artificial neural network. Jiang et al [15] established a mixed interval time-series prediction model to achieve the high-precision interval PM2.5 concentration prediction by considering the daily variation in pollutant concentrations.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Comparative Analysis of the Influence of Magnetic Field Effect on PM2.5 Trapping Efficiency in Electrostatic Precipitator (ESP) under Different Temperatures

Zhang,

Zhang

2023

Applied Sciences

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Charged particles have high momentum under high-temperature conditions, which helps to promote their movement towards a dust collector in a magnetic field environment, making it possible to improve the efficiency of the high-temperature wire-plate electrostatic precipitator (ESP) in this environment. A multi-field coupling model was established to numerically simulate PM2.5 dust-removal efficiency in an ESP under different working conditions. Combining the particle swarm optimization (PSO) algorithm with the support vector machine (SVM) model, the PSO-SVM prediction model is presented. Simulated data were used as training data, and PSO-SVM and back-propagation (BP) neural network models were utilized to predict collection efficiency under different working conditions, respectively. The results show that introducing a magnetic field can effectively improve the PM2.5 collection efficiency of wire-plate ESP, and the effect of a magnetic field on the dust-removal efficiency is more obvious at higher temperatures and higher flue gas velocities. When changing the working conditions, the predicted results of the magnetic field effect conform to simulated ones, and the PSO-SVM predicted values have a smaller relative error than those of the BP model, which can better adapt to different working conditions. All of the above conclusions can be utilized as a simple and adequately efficient example of the ESP model for follow-up research.

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A classification system based on improved global exploration and convergence to examine student psychological fitness

Shaikh,

Zheng,

Wang

et al. 2024

Sci Rep

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Anxiety is an important issue that affects their academic performance, mental health, and overall educational journey. To address this issue, it is important to accurately assess anxiety levels and provide evidence-based techniques. However, due to the complexity of anxiety and individual differences, analyzing clustering algorithms to efficiently classify psychological levels is challenging. Traditional clustering techniques face certain challenges in accurately classifying anxiety levels, such as slow convergence, sensitivity to initial conditions, and difficulties in handling constraints. To address these issues, clustering with an improved Mayfly-based optimization algorithm (IMOA) is proposed based on the dynamic variable for better performance to classify psychological levels. Initially, IMOA is validated using 23 standard benchmark functions, confirming its ability to find optimal solutions. Then, IMOA is applied to the student dataset, classifying them into Cluster A and Cluster B. The average scores for both clusters across all test cases are 76.7% and 53.07%, respectively. These results demonstrate the formation of dissimilar student groups with homogeneous emotions and performance, highlighting the importance of addressing emotional stress. Finally, by assigning students to clusters, educators and mental health professionals can better support those who may struggle, ensuring they receive the attention and resources they need. The obtained results show that IMOA with a dynamic variable effectively classifies student anxiety, improving the learning environment and helping teachers better understand students’ needs. This identification allows them to provide more effective support and adapt their teaching to meet the specific needs of those seeking support.

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Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network

Cited by 6 publications

References 27 publications

Displacement prediction of fine-grained tailings ponds based on WOA-BP neural network

Displacement prediction of fine-grained tailings ponds based on WOA-BP neural network

Prediction and Comparative Analysis of the Influence of Magnetic Field Effect on PM2.5 Trapping Efficiency in Electrostatic Precipitator (ESP) under Different Temperatures

A classification system based on improved global exploration and convergence to examine student psychological fitness

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