scholarly journals Application of BP neural network models in predicting the desulfurization rate of petroleum coke calcination flue gas

2020 ◽  
Vol 612 ◽  
pp. 012007
Author(s):  
Y W Lv ◽  
Y Zhu ◽  
Y X Jiang ◽  
L Xue ◽  
L Zuo
Keyword(s):  
Neural Network ◽  
Petroleum Coke ◽  
Bp Neural Network ◽  
Flue Gas ◽  
Network Models ◽  
Neural Network Models ◽  
Desulfurization Rate

scholarly journals Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO-BP Neural Networks

2021 ◽  
Vol 13 (23) ◽  
pp. 4801
Author(s):  
Hanlin Chen ◽  
Fei Niu ◽  
Xing Su ◽  
Tao Geng ◽  
Zhimin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Orbit Error ◽  
Broadcast Ephemeris ◽  
Bp Neural Network Model ◽  
Orbit Errors

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.

scholarly journals The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Liwen Zhang ◽  
Chao Zhang ◽  
Zhuo Sun ◽  
You Dong ◽  
Pu Wei
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Long Span ◽  
Long Span Bridge ◽  
Vibration Responses ◽  
Bp Neural Network Model

The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.

Modified BP Neural Network Model is Used for Odd-Even Discrimination of Integer Number

2013 ◽  
Vol 423-426 ◽  
pp. 2675-2678 ◽  
Author(s):  
Bao Long Hu ◽  
Ji Ren Xu ◽  
Huai Hui Gao ◽  
Ji Hai Liu ◽  
Ke Ren Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Bp Algorithm ◽  
Integer Number ◽  
Neural Network Models ◽  
Bp Neural Network Model ◽  
Bp Model

This paper introduced the BP neural network model and the BP algorithm in detail, and pointed out the BP neural network existed the defects of local optimal tendency of local optimal, slowed convergence speed etc. Through the modified BP algorithm, we could solve the problems existing in the traditional BP algorithm successfully, simulation results for odd-even discrimination of integer number based on MATLAB BP algorithm show that modified BP model compared with BP model has faster training speed and high study accuracy. Modified BP neural network models is used in practice, as long as it is complementary with effective measures, and we can get satisfactory result completely.

Modeling on Hydrocyclone Separation Performance by Neural Network

2011 ◽  
Vol 105-107 ◽  
pp. 185-188
Author(s):  
Feng Qin He ◽  
Ping Zhou ◽  
Jian Gang Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Structural Parameters ◽  
Input Parameter ◽  
Network Models ◽  
Separation Performance ◽  
Sampled Data ◽  
Neural Network Models

A 17-27-5 type BP neural network model was built, whose sampled data was got by hydrocyclone separation experiments; another 6-30-5 type BP neural network was also built, whose sampled data came from the simulation results of the LZVV of a hydrocyclone with CFD code FLUENT. The two neural network models also have good predictive validity aimed at hydrocyclone separation performance. It demonstrates LZVV structural parameters can embody hydrocyclone separation performance and reduce input parameter numbers of neural network model. It also indicates that the predictive model of hydrocyclone separation performance can be built by neural network.

scholarly journals BP Neural Network Calculus in Economic Growth Modelling of the Group of Seven

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 37 ◽  
Author(s):  
Xu Wang ◽  
JinRong Wang ◽  
Michal Fečkan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Reference Value ◽  
Network Models ◽  
Adaptive Lasso ◽  
Neural Network Models ◽  
Group Of Seven ◽  
Goods And Services ◽  
Total Capital ◽  
Bp Neural Network Model

In this paper, the adaptive lasso method is used to screen variables, and different neural network models of seven countries are established by choosing variables. Gross domestic product (GDP) is a function of land area in the country, cultivated land, population, enrollment rate, total capital formation, exports of goods and services, and the general government’s final consumption of collateral and broad money. Based on the empirical analysis of the above factors from 1973 to 2016, the results show that the BP neural network model has better performance based on multiple summary statistics, without increasing the number of parameters and better predicting short-term GDP. In addition, the change and the error of the model are small and have a certain reference value.

scholarly journals Application of BP Neural Network Model in Risk Evaluation of Railway Construction

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Changwei ◽  
Li Zonghao ◽  
Guo Xueyan ◽  
Yu Wenying ◽  
Jin Jing ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Construction Project ◽  
Network Models ◽  
Training Algorithms ◽  
Neural Network Models ◽  
Railway Construction ◽  
Railway Construction Project

Chinese railway construction project is an important part of the implementation of the “Belt and Road” strategy, and the risk evaluation of overseas railway construction is the primary link of the project. Firstly, this paper mainly analyzes the Asian and European countries along the railway construction project, establishes a railway construction project risk evaluation system, and synthesizes various risk factors. Secondly, it establishes two independent BP neural network models by using different training algorithms because of the different political, economic, and cultural elements between the two continents.

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