Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction

Meng Yue-bo(孟月波)^1,2, Zou Jian-hua(邹建华)¹, Gan Xu-sheng(甘旭升)³, LIU Guang-hui(刘光辉)²

(1. Systems Engineering Institute, School of Electronics and Information Engineering,
Xi’an Jiaotong University, Xi’an 710049, China;
2. School of Information and Control Engineering, Xi’an University of Architecture and Technology,
Xi’an 710055, China;
3. Engineering College, Air Force Engineering University, Xi’an 710038, China)

Abstract:In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.

Key words:wavelet; neural network; fuzzy C-means clustering; kernel principal components analysis; feature extraction; aerodynamic modeling