### 改进的RBF神经网络在翼梢小翼优化设计中的应用

1. 1. 西北工业大学 航空学院, 陕西 西安 710072;
2. 中国航空工业集团公司 第一飞机设计研究院, 陕西 西安 710089
• 收稿日期:2013-09-17 修回日期:2013-12-05 出版日期:2014-07-25 发布日期:2014-01-08
• 通讯作者: 白俊强，Tel.：029-88492174E-mail：junqiang@nwpu.edu.cn E-mail:junqiang@nwpu.edu.cn
• 作者简介:白俊强男，博士，教授，博士生导师。主要研究方向：飞行器总体设计。Tel：029-88492174E-mail：junqiang@nwpu.edu.cn；王丹女，博士研究生。主要研究方向：飞行器总体设计。Tel：13772122144E-mail：wangdan_hong@163.com；何小龙男，硕士研究生。主要研究方向：飞行器总体设计。Tel：029-88492174E-mail：173126638@qq.com；李权男，博士，工程师。主要研究方向：飞机气动设计、飞机多学科优化设计。Tel：029-86832360E-mail：lqq0309@163.com；郭兆电男，研究员，副总师。主要研究方向：飞行器总体气动设计。Tel：029-86832260E-mail：gzd0913@afai.cn
• 基金资助:

国家“863”计划（2012AA01A304）

### Application of an Improved RBF Neural Network on Aircraft Winglet Optimization Design

BAI Junqiang1, WANG Dan1, HE Xiaolong1, LI Quan2, GUO Zhaodian2

1. 1. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. AVIC The First Aircraft Institute, Xi'an 710089, China
• Received:2013-09-17 Revised:2013-12-05 Online:2014-07-25 Published:2014-01-08
• Supported by:

National High-tech Research and Development Program of China (2012AA01A304)

Abstract:

A self-adaptive radial basis function (RBF) neural network is proposed in order to improve the prediction accuracy of the original RBF. A self-adaptive vector with the same dimension as the sample vector is introduced into the traditional RBF network. In contrast to other RBF neural network models, the current approach achieves the self-adaptive construction of the network by altering the form of the basis function directly, which reduces the number of variables to be optimized. This adaptive approach substantially changes the impact of the center and width of the RBF neural network on its prediction as well as the influence of each variable of the independent vector on the dependent vector. Thus the introduced vector enables the adaptability of the RBF neural network with respect to variant problems. Moreover, the accuracy and the universality of the prediction model are also improved due to the optimization of the self-adaptive vector. The proposed self-adaptive RBF neural network is applied to a winglet optimization design of a wing-body-winglet configuration. The optimization objective is to minimize the cruise drag with wing-root bending moment restriction. The optimization result confirms the effectiveness and the capability for engineering application of the self-adaptive RBF neural network.