Soft Measurement Model of Passenger Vehicle Tire Mileage Based on GA-BP Neural Network

2021 ◽  
pp. 445-450
Author(s):  
Chao Shi ◽  
Bo Wang ◽  
Ning He
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Measurement Model ◽  
Passenger Vehicle ◽  
Soft Measurement

scholarly journals The Study on Void Fraction Prediction of Gas-liquid Two Phase Flow Based on Convolutional Neural Network

2021 ◽  
Vol 2121 (1) ◽  
pp. 012029
Author(s):  
Zhe Kan ◽  
Xinyang Liu
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Convolutional Neural Network ◽  
Void Fraction ◽  
Measurement Model ◽  
Learning Ability ◽  
Phase Flow ◽  
Two Phase ◽  
Soft Measurement ◽  
Accuracy And Stability

Abstract For the gas-liquid two phase flow in the horizontal pipeline, at the center angle the void fraction of the different liquid phases is calculated with the finite element simulation software, and then a soft measurement model of the void fraction is established. By comparing with traditional recursive augmented least squares (RELS), particle swarm optimization (PSO), and simulated annealing-based PSO, the void fraction soft measurement model is identified and calculated separately. The segmentation optimization results of PSO based on simulated annealing have higher accuracy and stability than RELS and PSO, but as the number of center angles increases, the relative accuracy and stability of the system will deteriorate. And the characteristic is not conducive to the calculation and analysis of data results. By combining the actual model, the convolutional neural network weight update algorithm is added to the LSTM, and the RNN-LSTM convolutional neural network is used to predict the void fraction of the second half the region. It improves the effect of RNN gradient problem on learning ability and improves learning ability. Through comparison, it is found that the convolutional neural network based on RNN-LSTM has a better prediction effect, improves the accuracy and stability of the system, and provides a new method for the measured void fraction of twophase flow.

scholarly journals Research on a soft-measurement model of gasification temperature based on recurrent neural network

Clean Energy ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 861-868
Author(s):  
Haiquan An ◽  
Xinhui Fang ◽  
Zhen Liu ◽  
Ye Li
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Bp Neural Network ◽  
Linear Prediction ◽  
Process Analysis ◽  
Back Propagation ◽  
Prediction Method ◽  
Measurement Model ◽  
Soft Sensor ◽  
Gasification Temperature

Abstract Gasification temperature measurement is one of the most challenging tasks in an entrained-flow gasifier and often requires indirect calculation using the soft-sensor method, a parameter prediction method using other parameters that are more easily measurable and using correlation equations that are widely accepted in the gasification field for the temperature data. Machine learning is a non-linear prediction method that can adequately act as a soft sensor. Furthermore, the recurrent neural network (RNN) has the function of memorization, which makes it capable of learning how to deal with temporal order. In this paper, the oxygen–coal ratio, CH4 content and CO2 content determined through the process analysis of a 3000-t/d coal-water slurry gasifier are used as input parameters for the soft sensor of the gasification temperature. The RNN model and back propagation (BP) neural network model are then established with training-set data from gasification results. Compared with prediction set data from the gasification results, the RNN model is found to be much better than the BP neural network based on important indexes such as the mean square error (MSE), mean absolute error (MAE) and standard deviation (SD). The results show that the MSE of the prediction set of the RNN model is 6.25°C, the MAE is 10.33°C and the SD is 3.88°C, respectively. The overall accuracy, the average accuracy and the stability effects are well within the accepted ranges for the results as such.

Analysis of consumer online resale behavior measurement based on machine learning and BP neural network

2020 ◽  
pp. 1-12
Author(s):  
Xinlu Zou
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bp Neural Network ◽  
Real Data ◽  
Measurement Model ◽  
Population Diversity ◽  
Training Models ◽  
Network Training ◽  
Data Prediction ◽  
Comparison Results

The reasons for consumers’ resale behavior are complex and sometimes diverse, and the investigation of consumer resale behavior is not a simple matter. Therefore, only through a lot of investigation and inquiry can we reach relevant conclusions. Based on machine learning and BP neural network, this paper constructs a consumer online resale behavior measurement model. The contraction-expansion factor can balance the global search and local search capabilities in different iteration periods, and the differential evolution operator is introduced to solve the problem of lack of population diversity. After building the model, this study collects data through questionnaires, and combines neural network training models to take data training and data prediction. In addition, this study compares and analyzes real data with predicted data, and visually displays the comparison results through statistical graphs. The results show that the method proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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