Hybrid Lateral Aerodynamic Modeling Based on WNN and Kernel Principal Components Feature Extraction

2013 ◽  
Vol 763 ◽  
pp. 242-245
Author(s):  
Xu Sheng Gan ◽  
Hao Lin Cui ◽  
Ya Rong Wu ◽  
Yue Bo Meng
Keyword(s):  
Neural Network ◽  
Principal Components ◽  
Model Parameters ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Training Samples ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
Basic Features ◽  
Better Than

In order to better describe the dynamic characteristics of aircraft through aerodynamic modeling, a Wavelet Neural Network (WNN) aerodynamic modeling method based on Kernel Principal Components Analysis (KPCA) is proposed. Firstly, the training samples are used to execute KPCA for extracting basic features of samples, and then using the extracted basic features, WNN aerodynamic model was established. The simulation result shows that, the modeling ability of the method proposed is better than that of another 3 methods. It can easily determine of model parameters. This enables it to be effective and feasible to establish the aerodynamic modeling for aircraft.

Research on Aviation Material with Unsafe Events Prediction Based on BP Neural Network with PCA Feature Extraction

2014 ◽  
Vol 540 ◽  
pp. 488-491 ◽  
Author(s):  
Xu Sheng Gan ◽  
Hua Ping Li ◽  
Jing Shun Duanmu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Aviation Material ◽  
Training Samples ◽  
Basic Features ◽  
Bp Neural Network Model ◽  
Better Than

In order to better predict the aviation material unsafe events, a BP neural network model based on PCA feature extraction is proposed. Firstly, the training samples of aviation material unsafe events are used to carry out the PCA feature extraction, and then using the extracted basic features, BP neural network model is established. The numerical example shows that, the hybrid model proposed is better than that of alone BP neural network model, and it is effective and feasible to establish the unsafe events model for aviation material.

Hopfield Neural Network Modeling Method Based on Subset KPCA with FCMC Clustering

2014 ◽  
Vol 1049-1050 ◽  
pp. 1658-1661
Author(s):  
Xiang Jie Luo ◽  
Xiao Yong Li ◽  
Hong Qu ◽  
Yue Bo Meng
Keyword(s):  
Neural Network ◽  
Nonlinear Modeling ◽  
Network Modeling ◽  
Hopfield Neural Network ◽  
Modeling Method ◽  
Neural Network Modeling ◽  
Fuzzy C Means Clustering ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
Better Than

In order to improve the performance of nonlinear modeling, a Hopfield neural network modeling method based on Subset Kernel Principal Components Analysis (SubKPCA) with Fuzzy C-Means Clustering (FCMC) is proposed. The simulation result shows that, the performance of the proposed method is better than that of Hopfield neural network based on KPCA. It also is effective and feasible to establish the model for the estimation of missing flight data.

Face Recognition Algorithm Based on Kernel Collaborative Representation

2013 ◽  
Vol 756-759 ◽  
pp. 3590-3595
Author(s):  
Liang Zhang ◽  
Ji Wen Dong
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Collaborative Representation ◽  
Nonlinear Structures ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
The Relationship

Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Analysis (KPCA) and Collaborative Representation Classifier (CRC) is developed. The KPCA can obtain effective discriminative information and reduce the feature dimensions by extracting faces nonlinear structures features, the decisive factor. Considering the collaboration among the samples, the CRC which synthetically consider the relationship among samples is used. Experimental results demonstrate that the algorithm obtains good recognition rates and also improves the efficiency. The KCRC algorithm can effectively solve the problem of illumination and occlusion in face recognition.

Physical Essence of Stall Phenomena and its WNN-Based Aerodynamic Modeling from Flight Data

2014 ◽  
Vol 602-605 ◽  
pp. 3140-3143
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Flight Data ◽  
Numerical Examples ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Aerodynamic Method ◽  
Physical Essence ◽  
Set Up

To understand the characteristics of aircraft stall for better aerodynamic model, the physical essence of the stall phenomena of aircraft is first introduced, and then a Wavelet Neural Network (WNN) is proposed to set up the stall aerodynamic model. Numerical examples indicates that through the deep cognition of the stall phenomena of aircraft the proposed stall aerodynamic method has a better accuracy than the traditional neural network and is also effective and feasible.

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