Path Tracking of Underground Mining Boom Roadheader Combining BP Neural Network and State Estimation

Qu, Yuanyuan; Teng, Yanguo; Li, Tao; Yu, Zhan; Fu, Shichen

doi:10.3390/app12105165

Cited by 6 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, Figure 7 shows that the prediction accuracy of the improved neural network is higher than that of BP neural network. Therefore, the simulation results show that the improved fuzzy neural network can effectively predict improving public sports effect under exercise intervention and has high prediction accuracy (Qu et al [ 20 ]). Therefore, in the future research, this paper will strengthen the research of physical exercise, especially the fitting analysis between data, and improve the accuracy of the analysis results.…”

Section: Resultsmentioning

confidence: 99%

Analysis and Research on the Impact of Physical Exercise on Residents’ Health Based on the Improved BP Neural Network Model

Wang

Liu

Zhou

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

With the rapid improvement of social economy and the enhancement of people’s health awareness, it is necessary to make an in-depth analysis of the rationality of physical exercise and the physical quality of residents. Hence, this study aims to explore the algorithm optimization of the improved BP model to analyze the effect of exercise intervention on improving public sports effect. K-clustering and Levenberg–Marquardt algorithm were used to construct an improved BP neural network model to determine the sample clustering center, as well as the weight and threshold of the indicators, so as to optimize the analysis algorithm of improving public sports effect. MATLAB simulation shows that under the target error conditions of 0.01, 0.005, 0.001, and 0.0001, the target error rate and iteration times of the improved BP model are better than the standard BP model, and the time consumption is shorter, which can be conducive to more accurately analyzing the changes of improving public sports effect under exercise intervention. Therefore, the improved BP model can effectively solve the problems of data clustering and result error rate adjustment in the process of improving public sports effect analysis and improve the analysis speed and accuracy.

show abstract

Section: Resultsmentioning

confidence: 99%

Analysis and Research on the Impact of Physical Exercise on Residents’ Health Based on the Improved BP Neural Network Model

Wang

Liu

Zhou

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

show abstract

“…Key factors such as train routes, timetables, infrastructure capacity, and potential disruptions can be incorporated into the model to enhance its predictive capabilities and robustness. Moreover, by utilizing historical data and real-time inputs, the model can continuously update and optimize schedules to account for changing conditions and unforeseen events [18].…”

Section: Introductionmentioning

confidence: 99%

Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network

Xiaoguang Hu

2024

jes

View full text Add to dashboard Cite

Rail transportation, a cornerstone of modern logistics and passenger transit systems, plays a pivotal role in facilitating the efficient movement of goods and people across vast distances. Operating on a network of interconnected tracks, rail systems offer a reliable and environmentally sustainable mode of transportation, particularly for long-distance travel and freight shipments. The paper presents a comprehensive investigation into the application of advanced computational techniques in the realm of rail transportation management. Specifically, Mamdani fuzzy logic and Backpropagation (BP) Neural Networks are employed to address critical challenges in scheduling and classification within rail networks. The utilization of Mamdani fuzzy logic facilitates nuanced decision-making in scheduling processes, considering uncertainties and complexities inherent in rail operations. Through linguistic rules and fuzzy sets, the scheduling system can effectively adapt to various operational constraints and disruptions, leading to more resilient and efficient scheduling solutions. Additionally, the integration of BP Neural Network enhances classification accuracy and prediction capabilities, enabling precise forecasting of train movements, passenger flows, and other key variables.

show abstract

“…Literature [8] proposed a method to convert the detection of similar and repeated records of text data into the detection of similar and repeated records of its binary strings; it provided the daily load data on mutual cleaning for the distribution network, showing the similarity of the daily cycle to ideas. Literature [9] proposed a power grid data cleaning and fusion algorithm based on a time series similarity measure, which uses symbol aggregation, the Euclidean algorithm, and similar sequences to adjust similarity weighting to complete cleaning; it uses a distributed Kalman filtering algorithm to complete data fusion. However, the cleaning algorithm requires a relatively large amount of calculation and is unsuitable for distributed small computing power equipment.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Joint Cleaning of Distribution Network Measurement Data Based on Correntropy Criterion with Variable Center Unscented Kalman Filter

Sun

Liu

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Measurement data cleaning is a key step of edge computing in a distribution network; it is beneficial to improve the state perception and regional autonomy level of a distribution network. According to the temporal and spatial correlation of measurement data in the distribution network, a joint cleaning method of measurement data in a distribution network is proposed based on the correntropy criterion with variable center unscented Kalman filter (CC-VC-UKF). Initially, the mean square error (MSE) in the original unscented Kalman filter (UKF) is replaced by the correntropy criterion with variable center (CC-VC) to improve the accuracy of filtering the measurement data in the distribution network with a non-Gaussian non-zero mean measurement deviation. Then, the measured data of different measuring devices located on the same section of the line are filtered based on the CC-VC-UKF algorithm according to their respective reference time series to improve the signal-to-noise ratio of the measured data. Then, the filtered measured data are filtered and cleaned based on the CC-VC-UKF algorithm according to the space–time joint filtering and cleaning technology. Finally, the method is used to test the measurement data of the distribution network obtained by a power supply company in a city in north China to solve the problem of measurement deviation caused by the existence of space distance. Results show that this method can obtain FTU measurement data with higher precision from network topology based on the filtered TTU measurement data through the media of filtered spatial measurement deviation.

show abstract

Path Tracking of Underground Mining Boom Roadheader Combining BP Neural Network and State Estimation

Cited by 6 publications

References 19 publications

Analysis and Research on the Impact of Physical Exercise on Residents’ Health Based on the Improved BP Neural Network Model

Analysis and Research on the Impact of Physical Exercise on Residents’ Health Based on the Improved BP Neural Network Model

Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network

Spatiotemporal Joint Cleaning of Distribution Network Measurement Data Based on Correntropy Criterion with Variable Center Unscented Kalman Filter

Contact Info

Product

Resources

About