scholarly journals Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Haorui Liu ◽  
Juan Yang ◽  
Heli Yang ◽  
Fengyan Yi
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Noise Suppression ◽  
Estimation Method ◽  
Principal Component ◽  
Soft Sensor ◽  
Kernel Principal Component Analysis ◽  
Elman Neural Network ◽  
On The Road ◽  
Vehicle State Estimation

In the car control systems, it is hard to measure some key vehicle states directly and accurately when running on the road and the cost of the measurement is high as well. To address these problems, a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network is proposed. Combining with nonlinear vehicle model of three degrees of freedom (3 DOF), longitudinal, lateral, and yaw motion, this paper applies the method to the soft sensor of the vehicle states. The simulation results of the double lane change tested by Matlab/SIMULINK cosimulation prove the KPCA-IENN algorithm (kernel principal component algorithm and improved Elman neural network) to be quick and precise when tracking the vehicle states within the nonlinear area. This algorithm method can meet the software performance requirements of the vehicle states estimation in precision, tracking speed, noise suppression, and other aspects.

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.

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