所属单位：

信息与控制工程学院

发表刊物：

Journal of Central South University of Technology

关键字：

中文关键字：Wavelet; Neural Network; Fuzzy C-Means Clustering;，英文关键字：Wavelet; Neural Network; Fuzzy C-Means Clustering;

摘要：

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 were selected from the training sample set to constitute a sample subset. Then, the obtained samples subset was used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model was established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of another 6 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.

备注：

孟月波

合写作者：

邹建华,甘旭升

第一作者：

刘光辉,孟月波

论文类型：

期刊论文

卷号：

卷：20

期号：

期：4

页面范围：

页：

是否译文：

否

发表时间：

2013-04-01