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Article | 09-April-2018

The Prediction of Haze Based on BP Neural Network and Matlab

In this paper, the neural network theory is used to establish the BP neural network prediction system for the occurrence of haze. The corresponding parameters are determined by MATLAB language, and the effect of the model is tested by the prediction of Shijiazhuang area. The result shows the feasibility of the predictive model. So it’s valuable and has a bright future.

Ma Limei, Wang Fangwei

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 2, 107–119

Article | 14-October-2020

Analysis and Forecast of Urban Air Quality Based on BP Neural Network

fault tolerance. In his research, Wang Jian pointed out that the BP neural network has advantages that other methods do not have in problems such as air quality prediction [6]. This paper uses air quality prediction based on BP neural network, and builds a neural network model to achieve air quality prediction, providing government environmental protection departments with air pollution trends. II. AIR QUALITY RELATED FACTORS AQI is the abbreviation of Air Quality Index. AQI does not refer to the

Wenjing Wang, Shengquan Yang

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 57–64

Article | 30-November-2018

Application of Improved BP Neural Network in Hybrid Control Model of Lime Quality

composition of raw material production of lime activity difference is very big, the different raw material must have the corresponding product quality requirements. Therefore, in order to make a correct judgment, the quality of the product must be compared with the corresponding product quality setting. BP neural network is composed of input layer (X), hidden layer (H) and output layer (Z). The hidden layer (H) can be one or more layers, and the article chooses only one layer of hidden layer of neural

Lingli Zhu, Tingzhong Wang

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 4, 83–86

Article | 01-September-2015


This paper focuses on the problem of lateral stability of four hub-motor-in-wheels drive electric vehicle, 7 DOF (degrees of freedom) vehicle simulation model which is verified by field test is established based on Matlab/Simulink software. On basis of simulated model, BP neural network PID torque distribution controller of lateral stability is proposed. The sideslip angle at mass center and yaw rate are selected as the control variables, and the BP neural network PID torque distribution

Biao Jin, Chuanyang Sun, Xin Zhang

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1855–1875

Article | 11-April-2018

Research on Combination Forecasting Model of Mine Gas Emission

This paper focuses on the effective analysis of the mine gas emission monitoring data, so as to realize the accurate and reliable mine gas emission prediction. Firstly, a weighted multiple computing models based on parametric t– norm is constructed. And a new mine gas emission combination forecasting method is proposed. The BP neural network model and the support vector machine were used as the single prediction models. Finally, genetic algorithm and least square method were used to

Liang Rong, Chang Xintan, Jia Pengtao, Dong Dingwen

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 194–198

Article | 01-September-2016


components of the localization information, and then regarding the nonlinear principal components extracted from distance vectors as the input samples, and meanwhile taking the coordinates of vertices in addition to the region boundary as the output samples, the PSO-BP neural network is trained to achieve the localization model. Finally the localization of unknown nodes can be estimated. The simulation experiment result showed that the method has high ability of stability and precision, and meets the

Wang Jun, Zhang Fu, Ren Tiansi, Chen Xun, Liu Gang

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 3, 1323–1340

Article | 09-April-2018

Prediction of the Heat Load in Central Heating Systems Using GA-BP Algorithm

This paper presented the research on heat load prediction method of central heating system. The combined simulation data at Xi'an in January was used as the samples for training and predicting. This paper selected the daily average outdoor wind speed, the daily average outdoor temperature, date type, sunshine duration as input variables and the heating load value as output variable. After preprocessing of the historical data, the BP neural network algorithm and the GA-BP algorithm were

Bingqing Guo, Jin Xu, Ling Cheng, Lei Chen

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 137–141

Article | 10-April-2018

Research on Intelligent Monitoring Technology of Micro Hole Drilling

Aiming at the problem that the micro drills is easy to be broken in the process of drilling; it is difficult to detect the drill bit. The drilling torque signal is taken as the monitoring object. A new method for the on-line monitoring the micro-drill breakage based on BP neural network is proposed. After the three layer wavelet decomposition of the drilling torque signal, the energy feature vector is used as the input layer of the BP network, and the mapping model of the working state and the

Yanhong Sun, Mei Tian

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 142–146

Research paper | 10-April-2013


, Least Squares Support Vector Machines (LS-SVM) is introduced. In order to estimate the effectiveness of feature selection algorithm, the comparative analysis among Fisher Score (FS) Information Gain (IG) and SVM-RFE is exploited to real milling datasets. The identification result proves that: The selected feature set based on SVM-RFE is more effective to recognize tool wear state; LS-SVM wear identification method is superior to BP neural network, and it has higher identification accuracy; the

Weilin Li, Pan Fu, Weiqing Cao

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 448–465

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