Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction

作者：Meng Yue-bo^*; Zou Jian-hua; Gan Xu-sheng; Liu Guang-hui

来源：Journal of Central South University, 2013, 20(4): 931-941.

DOI：10.1007/s11771-013-1568-x

• wavelet
• neural network
• fuzzy C-means clustering
• kernel principal components analysis
• feature extraction
• aerodynamic modeling

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.

• 出版日期2013-4
• 单位西安建筑科技大学; 中国人民解放军空军工程大学; 西安交通大学