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.

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.

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