Prediction Model of Passenger Waiting Time in High-Speed Rail Hub Based on Bp Neural Network

In order to explore the characteristics of passenger waiting time in high-speed rail hub, this paper analyzed the influencing factors of passenger waiting time, based on the survey of passenger waiting time in high-speed rail hub. And the main influencing factors were screened out using variance analysis. Then the prediction model of passenger waiting time based on BP neural network was established, the parameters of the model were calibrated and the validity was verified. The results show that, travel time in urban area, trip distance, familiarity toward the hub, educational background of passengers, and the type of transportation is the main influencing factor of passenger waiting time in high-speed rail hub, and the average relative error is only 9.2% using the proposed prediction model of passenger waiting time based on BP neural network.

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Prediction for Percentage of Vehicle Entering Expressway Rest Area Based on BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.178-181.1956 ◽

2012 ◽

Vol 178-181 ◽

pp. 1956-1960

Author(s):

Xiao Yan Shen ◽

Hao Xue Liu ◽

Jia Liu

Keyword(s):

Neural Network ◽

Prediction Model ◽

Influencing Factors ◽

High Precision ◽

Bp Neural Network ◽

High Speed ◽

High Speed Railway ◽

Neural Network Prediction ◽

Network Prediction ◽

Influence Degree

In order to scientifically decide the percentage of vehicle entering expressway rest area, based on analyzing the influencing factors relating to the percent of mainline traffic stopping, a BP neural network prediction model for it was put forward. Finally, The Xinzheng Rest Area (XRA) was taken as an example for verifying the feasibility of the prediction model and determining the influence degree of the Shijiazhuang-Wuhan high-speed railway on the percentage of mainline vehicles entering XRA. The result shows that the model had a high precision and reliability.

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Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Machines ◽

10.3390/machines9110286 ◽

2021 ◽

Vol 9 (11) ◽

pp. 286

Author(s):

Zhaolong Li ◽

Bo Zhu ◽

Ye Dai ◽

Wenming Zhu ◽

Qinghai Wang ◽

...

Keyword(s):

Neural Network ◽

Prediction Model ◽

Bp Neural Network ◽

High Speed ◽

Search Algorithm ◽

Thermal Error ◽

Error Prediction ◽

Machining Accuracy ◽

Motorized Spindle ◽

Thermal Error Model

High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error.

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Optimal design of noise reduction and shape modification for traction gears of EMU based on improved BP neural network

Noise Control Engineering Journal ◽

10.3397/1/376934 ◽

2021 ◽

Vol 69 (4) ◽

pp. 373-388

Author(s):

Zhaoping Tang ◽

Min Wang ◽

Xiaoying Xiong ◽

Manyu Wang ◽

Jianping Sun ◽

...

Keyword(s):

Neural Network ◽

Prediction Model ◽

Bp Neural Network ◽

High Speed ◽

Transmission System ◽

Gear Transmission ◽

Noise Prediction ◽

Vibration Acceleration ◽

Acceleration Level ◽

Gear Transmission System

Under high-speed operating conditions, the noise caused by the vibration of the traction gear transmission system of the Electric Multiple Units (EMU) will distinctly reduce the comfort of passengers. Therefore, analyzing the dynamic characteristics of traction gears and reducing noise from the root cause through comprehensive modification of gear pairs have become a hot research topic. Taking the G301 traction gear transmission system of the CRH380A high-speed EMU as the research object and then using Romax software to establish a parametric modification model of the gear transmission system, through dynamics, modal and Noise Vibration Harshness (NVH) simulation analysis, the law of howling noise of gear pair changes with modification parameters is studied. In the small sample training environment, the noise prediction model is constructed based on the priority weighted Back Propagation (BP) neural network of small noise samples. Taking the minimum noise of high-speed EMU traction gear transmission as the optimization goal, the simulated annealing (SA) algorithm is introduced to solve the model, and the optimal combination of modification parameters and noise data is obtained. The results show that the prediction accuracy of the prediction model is as high as 98.9%, and it can realize noise prediction under any combination of modification parameters. The optimal modification parameter combination obtained by solving the model through the SA algorithm is imported into the traction gear transmission system model. The vibration acceleration level obtained by the simulation is 89.647 dB, and the amplitude of the vibration acceleration level is reduced by 25%. It is verified that this modification optimization design can effectively reduce the gear transmission.

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An Improved Prediction Model of IGBT Junction Temperature Based on Backpropagation Neural Network and Kalman Filter

Complexity ◽

10.1155/2021/5542889 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Yu Dou

Keyword(s):

Neural Network ◽

Kalman Filter ◽

Prediction Model ◽

Bp Neural Network ◽

High Speed ◽

Rapid Development ◽

Junction Temperature ◽

Insulated Gate Bipolar Transistor ◽

Network Estimation ◽

The Stability

With the rapid development of emerging technologies such as electric vehicles and high-speed railways, the insulated gate bipolar transistor (IGBT) is becoming increasingly important as the core of the power electronic devices. Therefore, it is imperative to maintain the stability and reliability of IGBT under different circumstances. By predicting the junction temperature of IGBT, the operating condition and aging degree can be roughly evaluated. However, the current predicting approaches such as optical, physical, and electrical methods have various shortcomings. Hence, the backpropagation (BP) neural network can be applied to avoid the difficulties encountered by conventional approaches. In this article, an advanced prediction model is proposed to obtain accurate IGBT junction temperature. This method can be divided into three phases, BP neural network estimation, interpolation, and Kalman filter prediction. First, the validities of the BP neural network and Kalman filter are verified, respectively. Then, the performances of them are compared, and the superiority of the Kalman filter is proved. In the future, the application of neural networks or deep learning in power electronics will create more possibilities.

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Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm

Mathematical Problems in Engineering ◽

10.1155/2021/6595783 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Nan Zhao ◽

Sang-Bing Tsai

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Influencing Factors ◽

Bp Neural Network ◽

Tourism Development ◽

Development Economic ◽

Bp Artificial Neural Network ◽

Relationship Analysis ◽

Artificial Neural

Due to the lack of macro and systematic data, the target cost of high-star hotel project cannot meet the characteristics and needs of the hotel project itself. Therefore, the establishment of star hotel development scale prediction is urgent. In the scale development strategy, based on the previous studies, combined with the development characteristics of regional high-star hotels in a city, this paper constructs the index system of influencing factors of the development scale of high-star hotels and extracts the main influencing factors of hotel development scale by principal component analysis and partial relationship analysis, which are mainly urban development, economic development, tourism development, tourism development exhibition industry development, business development, and transportation development. The BP artificial neural network prediction method is used to establish a prediction model for the development scale of high-star hotels, by adopting the above key extraction factors as input of BP neural network. Through the input and output of the scale influence index data, the development scale of star hotels is accurately predicted. The simulation results verify the effectiveness and reliability of the star hotel development scale prediction strategy based on BP neural network, in terms of accuracy and model superiority.

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An Optimal Design Algorithm for Proppant Placement in Slickwater Fracturing

10.2118/207617-ms ◽

2021 ◽

Author(s):

HaiYang Wang ◽

Desheng Zhou ◽

Jinze Xu ◽

Shun Liu ◽

Erhu Liu ◽

...

Keyword(s):

Neural Network ◽

Optimal Design ◽

Prediction Model ◽

Influencing Factors ◽

Bp Neural Network ◽

Unconventional Reservoirs ◽

Hydraulic Fractures ◽

Oil Well ◽

Design Algorithm ◽

Slickwater Fracturing

Abstract Slickwater fracturing technology is one of the significant stimulation measures for the development of unconventional reservoirs. An effective proppant placement in hydraulic fractures is the key to increase the oil production of unconventional reservoirs. However, previous studies on optimizing proppant placement are mainly focused on CFD numerical simulation and related laboratory experiments, and an optimization design method that comprehensively consider multiple influencing factors has not been established. The objective of this study is to establish an optimal design algorithm for proppant placement based on the construction characteristics of slickwater fracturing combined with Back Propagation (BP) neural network. In this paper, a proppant placement simulation experimental device was built to analyze proppant placement form data. We established a BP neural network model that considers multiple influencing factors and used the proppant placement form data to train and calibrate the model, which the proppant placement form prediction model is finally obtained. Using the proppant placement form prediction model, we designed an algorithm that can quickly select the three groups of construction schemes with the best proppant-filling ratio based on the massive construction schemes. The results indicate that the prediction results of the algorithm for proppant placement form are consistent with the CFD simulation results and experimental results, and the numerical error of the balanced height and the distance between the front edge of the proppant sandbank and the fracture entrance is within 5%. After using this algorithm to optimize the design of the fracturing construction scheme for the C8 oil well in Changqing Oilfield, the stimulation performance of the C8 oil well after fracturing is 2.7 times that of the adjacent well. The optimal design algorithm for proppant placement established in this paper is an effective, accurate, and intelligent optimization algorithm. This algorithm will provide a novel method for hydraulic fracturing construction design in oilfields.

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