Determination of Difficult Parking Points in Train Running Section Based on UAS and BP Neural Network

2019 ◽  
Author(s):  
Hongyu Zhou ◽  
Jiahui Feng ◽  
Jun Shen ◽  
Yang Chai ◽  
Qingyuan Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network

scholarly journals Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chun Yan ◽  
Meixuan Li ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Determination ◽  
Oil Analysis ◽  
Dissolved Gas ◽  
Adaboost Algorithm ◽  
Diagnosis Model ◽  
The Stability ◽  
Transformer Fault

Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.

Simultaneous Determination of p-Nitrochlorobenzene and o-Nitrophenol in Mixture by Single-Sweep Oscillopolarography Based on Cascade Neural Network

2011 ◽  
Vol 217-218 ◽  
pp. 1469-1474
Author(s):  
Shuo Duan ◽  
Shuai Xu ◽  
Xiao Meng Xu ◽  
Xin Zhang ◽  
Chang Li Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Simultaneous Determination ◽  
Network Structure ◽  
Bp Neural Network ◽  
The Novel ◽  
Wavelet Neural Networks ◽  
Cascade Neural Network ◽  
Concentration Prediction

By combining the improved wavelet neural network and BP neural network, a new structure based on mixed cascade neural network was established. The novel cascade neural network has been used to the oscillopolargriphic signals analysis. By the figure fitting and parameters extracting, we realized the prediction of the simulation samples.The training speed and the predication accuracy can be enhanced by optimizing the network structure and parameters. The result of concentration prediction is satisfied . The method has been applied to the simultaneous determination of p- Nitrochlorobenzene (p-NCB) and o-Nitrophenol (o-NP) in simulation samples with satisfactory results. The Relative error and Recovery of p-NCB、o-NP were 3.76%、96.2%； 4.05%、96.0%, respectively. This novel cascade neural network combines the advantage of wavelet neural networks and BP neural networks, and performs its own functions respectively. It has shown a unique advantage in the overlap peak analyze.

scholarly journals Energy-saving technology of BIM green buildings using fractional differential equation

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ya Qin ◽  
Mohammed Basheri ◽  
Nympha Rita Joseph
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Energy Savings ◽  
Optimal Order ◽  
First Order ◽  
Model Based ◽  
Fractional Differential ◽  
Energy Saving Technology

Abstract In order to solve the problem of the traditional gray prediction model (GM) during determination of the accuracy of buildings’ energy savings and its poor fitting of data, the idea of a fractional model based on the traditional first-order one-variable GM(1,1) model is applied. We use the GM–backpropagation (GM-BP) neural network to solve the optimal fractional order and establish a fractional GM(1,1) model based on the GM-BP neural network. Example calculation shows that the fractional GM(1,1) model can improve the prediction accuracy of buildings’ energy savings, and selecting the optimal order can further improve the prediction accuracy and decrease the error level when using the GM-BP neural network. This work shows that the fractional GM(1,1) model based on the GM-BP neural network has an important guiding role in the energy savings of buildings.

scholarly journals Determination of Fiber Contents in Blended Textiles by NIR Combined with BP Neural Network

ISRN Textiles ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-5
Author(s):  
Li Liu ◽  
Li Yan ◽  
Yaocheng Xie ◽  
Jie Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Fiber Content ◽  
Cotton Wool ◽  
Near Infrared Spectra ◽  
Hidden Neurons

Fiber contents in cotton/terylene and cotton/wool blended textiles were tested by near infrared (NIR) spectroscopy combined with back propagation (BP) neural network. Near infrared spectra of samples were obtained in the range of 4000 cm−1~10000 cm−1. Wavelet Transform (WT) was used for noise reduction and compression of spectra data. The correction models of cotton/terylene and cotton/wool contents based on BP neural network and reconstructed spectral signals were established. The number of hidden neurons, learning rate, momentum factor, and learning times was optimized, and decomposition scale of WT was discussed. Experimental results have shown that this approach by Fourier transformation NIR based on the BP neural network to predict the fiber content of textile can satisfy the requirement of quantitative analysis and is also suitable for other fiber content measurements of blended textiles.

scholarly journals Determination of Fetal Left Ventricular Volume Based on Two-Dimensional Echocardiography

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Li Yu ◽  
Yi Guo ◽  
Yuanyuan Wang ◽  
Jinhua Yu ◽  
Ping Chen
Keyword(s):  
Neural Network ◽  
Correlation Coefficient ◽  
Bp Neural Network ◽  
Left Ventricular ◽  
Lv Function ◽  
Two Dimensional ◽  
Lv Volume ◽  
Dimensional Echocardiography ◽  
Two Dimensional Echocardiography

Determination of fetal left ventricular (LV) volume in two-dimensional echocardiography (2DE) is significantly important for quantitative analysis of fetal cardiac function. A backpropagation (BP) neural network method is proposed to predict LV volume more accurately and effectively. The 2DE LV border and volume are considered as the input and output of BP neural network correspondingly. To unify and simplify the input of the BP neural network, 16 distances calculated from the border to its center with equal angle are used instead of the border. Fifty cases (forty frames for each) were used for this study. Half of them selected randomly are used for training, and the others are used for testing. To illustrate the performance of BP neural network, area-length method, Simpson’s method, and multivariate nonlinear regression equation method were compared by comparisons with the volume references in concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC), and Bland-Altman plots. The ICC and CCC for BP neural network with the volume references were the highest. For Bland-Altman plots, the BP neural network also shows the highest agreement and reliability with volume references. With the accurate LV volume, LV function parameters (stroke volume (SV) and ejection fraction (EF)) are calculated accurately.

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