A BP Neural Network Prediction Model of the Urban Air Quality Based on Rough Set

2008 ◽  
Author(s):  
Zhifang Jiang ◽  
Xiangxu Meng ◽  
Chenglei Yang ◽  
Guansong Li
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Prediction Model ◽  
Rough Set ◽  
Bp Neural Network ◽  
Urban Air Quality ◽  
Urban Air ◽  
Neural Network Prediction ◽  
Network Prediction

Energy Consumption Prediction Model of SiCp/Al Composite in Grinding Based on PSO-BP Neural Network

2020 ◽  
Vol 305 ◽  
pp. 163-168
Author(s):  
Peng Gu ◽  
Chuan Min Zhu ◽  
Yin Yue Wu ◽  
Andrea Mura
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Swarm Optimization ◽  
Al Composite ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

As the typical particle-reinforced aluminum matrix composite, SiCp/Al composite has low density, high elastic modulus and high thermal conductivity, and is one of the most competitive metal matrix composites. Grinding is the main processing technique of SiCp/Al composite, energy consumption of the grinding process provides guidance for the energy saving, which is the aim of green manufacturing. In this paper, grinding experiments were designed and conducted to obtain the energy consumption of the grinding machine tool. The Particle Swarm Optimization (PSO) BP neural network prediction model was applied in the energy consumption prediction model of SiCp/Al composite in grinding. It showed that the Particle Swarm Optimization (PSO) BP neural network prediction model has high prediction accuracy. The prediction model of energy consumption based on PSO-BP neural network is helpful in energy saving, which contributes to greening manufacturing.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

2020 ◽  
Vol 13 (4) ◽  
pp. 657-671
Author(s):  
Wei Jiang ◽  
Hongmei Xu ◽  
Elnaz Akbari ◽  
Jiang Wen ◽  
Shuang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Principal Component ◽  
Spectral Difference ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

The Use of BP Neural Network in the Landslide Prediction of Three Gorges Reservoir

2013 ◽  
Vol 838-841 ◽  
pp. 2179-2184
Author(s):  
Hai Tao Long ◽  
Guo Dong Zhang ◽  
Jian Long Cao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Three Gorges Reservoir ◽  
Surface Displacement ◽  
Three Gorges ◽  
Landslide Prediction ◽  
Neural Network Prediction ◽  
Landslide Deformation ◽  
Network Prediction

Landslide prediction was one of the important tasks in the monitoring and early warning of the Three Gorges reservoir. On the basic of the BP neural network which was combined with the algorithm model, we took the typical Baishuihe landslide in Three Gorges reservoir for example. With the use of GPS monitoring data in surface displacement and its predisposing factors which was reservoir level, rainfall and so on, we built BP neural network prediction model. The monitoring of data recent years was used in the training and validation of the network .The results show that the BP neural network prediction model prediction can be used in the prediction of landslide deformation and the accuracy is high.

Establishment and Application of BP Neural Network Prediction Model of Xinxue River Constructed Wetland, China Based on Data Expansion

2012 ◽  
Vol 518-523 ◽  
pp. 1161-1164
Author(s):  
Qun Miao ◽  
Yu Ting Gu ◽  
Song Xue ◽  
Xi Peng Wang ◽  
Lei Bian
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Constructed Wetland ◽  
Bp Neural Network ◽  
Model Accuracy ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Data Expansion ◽  
Course Of Study

Over the course of study the BPNN prediction model of Xinxue River constructed wetland, model boundary was found as an important restrict factor of model accuracy. In the paper data expansion was selected to solve this problem, and the extended data was used to re-built the prediction model, results showed that the performance of model was effectively improved.

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

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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