Determination of Easy Parking Points of Train Driving Interval Based on UAS and BP Neural Network Linear Grey system

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

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.

The Combination of Grey System and BP Neural Network

2007 ◽  
Author(s):  
Fan Shuhai ◽  
Fang Yexiang ◽  
Li Wenping ◽  
Ma Yungao ◽  
Xiao Tianyuan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Grey System

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.

Vertical Deformation Prediction Method for Bridges Based on Grey System-BP Neural Network Model

2014 ◽  
Vol 578-579 ◽  
pp. 1217-1223
Author(s):  
Ming Jun Li ◽  
Huan Huan Wu ◽  
Xiao Feng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Vertical Deformation ◽  
Grey System ◽  
Single Model ◽  
Deformation Prediction ◽  
Input Sample ◽  
Monitoring And Forecasting ◽  
Bp Neural Network Model

For there are many problems such as large amount interference factors, the amount of data collected is small, and so on occur in monitoring and forecasting, use the model that combine grey system and neural network to forecast. That is predicting with single model first, and then taking the prediction result of grey system as input sample value of BP neural network. Considering the measured values as objective sample value of network, neural network used to deformation forecast can be obtained through training. Prediction results show that this method can obtain a good forecasting result.

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