scholarly journals Research on the Route Pricing Optimization Model of the Car-Free Carrier Platform Based on the BP Neural Network Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu-Ang Du
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Goodness Of Fit ◽  
Rapid Development ◽  
Free Carrier ◽  
Pricing Model ◽  
Logistics Industry ◽  
Characteristic Index ◽  
Pricing Optimization

The car-free carrier platform is a product of the rapid development of the modern logistics industry and has a vital strategic value for promoting the construction of a country’s comprehensive transportation. However, due to the unreasonable platform pricing model, the industry is currently in a bottleneck period. In order to solve this problem, we established a gray correlation model to calculate the degree of correlation between each characteristic index and platform pricing based on the massive historical transaction data of a certain platform and performed K-means clustering on the results to discover the main factors affecting platform pricing. Based on the abovementioned results, we created a pricing optimization model based on the BP neural network, with the structure of 8-13-1 to predict the freight pricing of the order and test the prediction results. The test shows that the goodness of fit (R2) of the predicted value is close to 1, and the prediction error range is less than 3.7%, which proves the accuracy and effectiveness of the BP neural network model and provides an effective reference for the optimization of the pricing model of the car-free carrier platform.

BP Neural Network Research Based on Three Convergence Improved LM Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1543-1546 ◽  
Author(s):  
Xiu Cai Guo ◽  
Sai Hua Shang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Goodness Of Fit ◽  
Optimization Theory ◽  
Mathematical Optimization ◽  
National Sample ◽  
Convergence Time ◽  
Practical Application ◽  
Application Problem ◽  
Lm Algorithm

In order to solve the practical application problem, which traditional neural network takes too long and compute complexly, on the basis of the LM algorithm, combined with mathematical optimization theory, identify the three convergence Improved LM algorithm applied to BP neural network , that improved LMBP algorithm. Simulation results show that the improved LMBP algorithm in convergence time and goodness of fit both have better results, and the algorithm is general and can be produced by obtaining national sample of various scenarios, using the algorithm to predict, to better guidance on production.

scholarly journals An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Development ◽  
Automatic Monitoring ◽  
Deformation Monitoring ◽  
Open Pit Mine ◽  
Slope Deformation ◽  
Open Pit ◽  
Gis Technology ◽  
Monitoring Method

At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value

Neural network-based optimization model for sprint data collection

2021 ◽  
pp. 1-11
Author(s):  
Guangfei Luo
Keyword(s):  
Neural Network ◽  
Data Collection ◽  
Data Acquisition ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Balance Control ◽  
Performance Parameters ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm

Sprint data has the characteristics of quality and continuity, but due to the limitations of optimization algorithm, the existing sprint data acquisition optimization model has the problem of low optimization performance parameters. Therefore, a data acquisition control optimization model based on neural network is proposed. This paper analyzes the advantages and disadvantages of neural network algorithm, combined with the sprint data collection optimization requirements, introduces BP neural network algorithm, based on this, uses multiple sensors, based on baud interval balance control to collect sprint data, applies BP neural network algorithm to compress, integrate and classify sprint data, realizes the sprint data collection and optimization. The experimental results show that the optimization performance parameters of the model are large, which fully shows that the model has good data acquisition optimization performance.

Risk Evaluation of the Wind Power Project Investment Based on BP Neural Network

2010 ◽  
Vol 108-111 ◽  
pp. 256-261 ◽  
Author(s):  
Wei Li ◽  
Shi Chao Li ◽  
Dan Wang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Rapid Development ◽  
Evaluation Index System ◽  
Project Investment ◽  
High Profit ◽  
Risk Characteristics ◽  
Power Project

With the rapid development of the society, more and more countries have been increasingly optimistic about wind power projects because of its advantages, such as non-polluting, renewable, energy-saving and emission reduction. While facing the temptation of high profit, it is necessary to assess the risks of wind power project investment scientifically. Therefore, this article combines with the risk characteristics of wind power project under the current social environment to build a evaluation index system of wind power project to evaluate the risk of wind power project based on BP neural network.

Study on the Irrigation Measurement and Prediction of Greenhouse Flowers under Water Stress Acoustic Emission Based on Neural Network

2013 ◽  
Vol 347-350 ◽  
pp. 985-989
Author(s):  
Huan Zou ◽  
Xin Wang ◽  
Lin Lin Yang ◽  
Yan Xin Yang ◽  
Xue Ping Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Irrigation System ◽  
Rapid Development ◽  
Basic Principles ◽  
Processing Ability ◽  
Bp Neural Network Model ◽  
Self Learning ◽  
Irrigation Volume ◽  
Precision Irrigation

Recently, with the rapid development of Chinese flower market, the precision irrigation problem is generally concerned. This paper mainly introduces the basic principles of the automatic irrigation system, and realizes the automatic control of the irrigation volume in irrigation system by utilizing the self-learning characteristics of BP neural network model and the powerful data processing ability of the matlab software.

Agricultural Machinery Spare Parts Demand Forecast Based on BP Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 1822-1825 ◽  
Author(s):  
Yao Guang Hu ◽  
Shuo Sun ◽  
Jing Qian Wen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Development ◽  
Spare Parts ◽  
Agricultural Machinery ◽  
Demand Forecast ◽  
The Third ◽  
Neural Network Prediction ◽  
Network Prediction

With the rapid development of agricultural machinery, forecasting the demand for spare parts is essential to ensure timely maintenance of agricultural machinery. Based on features of spare parts, BP neural network is chosen to forecast the demand. First, this paper analyzes factors that affect the demand for spare parts. Second, steps and processes of neural network prediction are described. The third part of this paper is case study based on certain brand of agricultural machinery spare parts. BP neural network turns out suitable for forecasting the demand for spare parts.

scholarly journals Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Maohua Xiao ◽  
Weichen Wang ◽  
Kaixin Wang ◽  
Wei Zhang ◽  
Hengtong Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
High Power ◽  
Bp Neural Network ◽  
Information Exchange ◽  
Rapid Development ◽  
Particle Swarm ◽  
Particle Swarm Optimizer ◽  
Under Five

With the rapid development of high-power tractor, the fault diagnosis of high-power tractor has become more and more important for ensuring the operating safety and efficiency. PSO is an iterative optimization evolutionary algorithm, which can iterate through different particles to find the optimal solution. However, there is only one population in the standard PSO algorithm, and the information exchange between the populations is relatively single, which can easily lead to the stagnation of the development of the population. In this paper, due to high-power tractor diesel engine fault complexity, fault correlation, and multifault concurrency, a multigroup coevolution particle swarm optimization BP neural network for diesel engine fault diagnosis method was proposed. First, the USB-CAN device was used to collect data of 8 items of the diesel engine under five different working conditions, and the data was parsed through the SAE J1939 protocol; then, the BP neural network was reconstructed, and a competitive multiswarm cooperative particle swarm optimizer algorithm (COM-MCPSO) was used to optimize its structure and weights. Finally, the data of optimized neural network under five different fault conditions show that, compared with BP neural network and PSO optimized BP neural network, the fault diagnosis of COM-MCPSO optimized BP neural network not only improves the network training speed, but also enhances generalization ability and improves recognition accuracy.

scholarly journals Optimization of Vehicle Transportation Route Based on IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qian Yu ◽  
Yuanguo Wang ◽  
Xiaogang Jiang ◽  
Bailu Zhao ◽  
Xiuling Zhang ◽  
...  
Keyword(s):  
Customer Satisfaction ◽  
Optimization Model ◽  
Fitness Function ◽  
Rapid Development ◽  
Route Optimization ◽  
Distribution Centers ◽  
Logistics Industry ◽  
Distribution Costs ◽  
Mutation Operators ◽  
Time Sensitivity

With the rapid development of logistics industry, optimization of road transport has become a constraint that must be overcome in the development of related industries. In the IoT era, classic car routing solutions could not meet many different needs. The relevant research findings are endless but not suitable to reduce costs in logistics and distribution processes and meet the needs of customers. This paper researches on vehicle path optimization using IoT technology and intelligent algorithms. Firstly, the traditional GA is optimized, and its coding mode, fitness function, selection, crossover, and mutation operators are studied. The crossover probability was set to 0.6, and the mutation probability was set to 0.1; then, according to the improved GA, a vehicle route optimization model was created. Finally, simulations were conducted to optimize vehicle routes for some distribution centers and 15 customer sites, and the model’s validity was tested. Experimental data show that the improved genetic algorithm begins to converge in 100 generations with a running time of 37.265 s. We calculate the time sensitivity of the customer. An algorithmic model is then used to determine distribution plans based on product demand and time sensitivity. In addition, we compare distribution costs and customer satisfaction of algorithmic and randomized plans. The distribution cost and customer satisfaction of the algorithmic and random patterns were 498.09 yuan and 573.13 yuan and 140.45 and 131.35, respectively. This shows that the vehicle routing optimization model using IoT technology and an improved GA can reduce distribution costs and increase customer satisfaction.

scholarly journals Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiancheng Liu ◽  
Congxiang Tian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Large Scale ◽  
Rapid Development ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Fast Training ◽  
Simulation Calculation ◽  
Monitoring Platform

With the rapid development of network technology, people are increasingly dependent on the internet. When BP neural network (BNN) performs simulation calculation, it has the advantages of fast training speed, high accuracy, and strong robustness and is widely used in large-scale public (LSP) building energy consumption (BEC) monitoring platforms (LPB). Therefore, the purpose of this paper to study the energy consumption monitoring platform of large public (LP) buildings is to better monitor the energy consumption of public buildings, so as to supplement or remedy at any time. This article mainly uses the data analysis method and the experimental method to carry on the relevant research and the system test to the BNN. The experimental results show that the monitoring system (MS) platform designed in this paper has real-time performance, and its time consumption is between 2 s and 3 s, and the data accords with theory and reality.

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