scholarly journals An Early Risk Warning of Cross-Border E-Commerce Using BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhichao Deng ◽  
Meiji Yan ◽  
Xu Xiao
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Bp Neural Network ◽  
Risk Index ◽  
Principal Component ◽  
Absolute Error ◽  
Kernel Principal Component Analysis ◽  
Cross Border ◽  
Risk Warning ◽  
Proposed Model

In this paper, we propose an early warning model of credit risk for cross-border e-commerce. Our proposed model, i.e., KPCA-MPSO-BP, is constructed using kernel principal component analysis (KPCA), improved particle swarm optimization (IPSO), and BP neural network. Initially, we use KPCA to reduce the credit risk index for cross-border e-commerce. Next, the inertia weight and threshold of BP neural network are searched using MPSO. Finally, BP neural network is used for training the data of 13 different enterprises of cross-border e-commerce’s credit risk. To analyze the efficiency of our proposed approach, we use the data of five different enterprises for testing and evaluation. The experimental results show that the mean absolute error (MAE) and root mean square error (RMSE) of our model are the lowest in comparison to the existing models and have much better efficiency.

scholarly journals A Method for Short-Term Prediction of the Metro Station’s Individual Energy Consumption Item Based on G-ACO-BP Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guorong Sha ◽  
Qing Qian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Air Conditioning ◽  
Principal Component ◽  
Neural Model ◽  
Primary Energy ◽  
Kernel Principal Component Analysis ◽  
Short Term ◽  
Metro Station

This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated air conditioning in the metro station. First, the paper extracts the five main factors influencing metro station energy consumption through the kernel principal component analysis (KPCA). Second, improved genetic-ant colony optimization (G-ACO) was fused into the BP neural network to train and optimize the connection weights and thresholds between each BP neural network layer. The paper then builds a G-ACO-BP neural model to make short-term predictions about different energy consumption in the metro station to predict the energy consumed by power, lighting, and ventilated air conditioning. The experimental results showed that the G-ACO-BP neural model could give a more accurate and effective prediction for the main energy consumption in a metro station.

Quality Prediction Model Based on PCA-BP Neural Network for Tobacco Leaves Redrying Process

2011 ◽  
Vol 201-203 ◽  
pp. 1627-1631
Author(s):  
Jian Kang Yin ◽  
Chang Hua Chen ◽  
Jing Min Li ◽  
Fei Zhang ◽  
Jin Yao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Absolute Error ◽  
Quality Prediction ◽  
Tobacco Leaves ◽  
Model Based ◽  
Input Variables

Aiming to the problem that is very difficult to establish the mechanism model of quality for the process of tobacco leaves redrying, this paper proposes a quality prediction model based on principal component analysis (PCA) and improved back propagation (BP)neural network for tobacco leaves redrying process. Firstly, 12 input variables are confirmed by analyzing the factors on quality of tobacco leaves redrying process. Second, the methods of PCA is used to eliminate the correlation of original input layer data, in which 12 input variables are transformed into 6 uncorrelated indicators. Then, the quality prediction model based on improved BP neural network is established. Finally, a simulation experiment is conducted and the average prediction error is as low as 1.03%, the absolute error for forecasting is fluctuated in the range of 0.16% - 2.49%. The result indicates that the model is simpler and has higher stability for prediction, which can completely meet the actual requirements of the tobacco leaves redrying process.

Multi-View Face Recognition Based on BP Neural Network and KPCA

2014 ◽  
Vol 1030-1032 ◽  
pp. 1737-1740
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Principal Component ◽  
Experimental Results ◽  
Kernel Principal Component Analysis ◽  
Face Images ◽  
Network Methods

This paper introduces the research background of computer face recognition technology, and puts forward a method of using kernel principal component analysis (KPCA) method and improved BP neural network methods for analysis and identification of multi view face images. The experimental results show that this algorithm is both effective and accurate. It achieved a higher recognition rate and excellent resistance to noise.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

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