飞机多学科设计优化中改进的径向基神经网络法  

An Improved Radial Basis Function Neural Network Method in Aircraft Multidisciplinary Design Optimization

作　　者：张健[1] 李为吉[1] 

机构地区：[1]西北工业大学航空学院,陕西西安710072

出　　处：《空军工程大学学报（自然科学版）》2006年第4期1-3,共3页Journal of Air Force Engineering University(Natural Science Edition)

基　　金：国家自然科学基金资助项目(10377015)

摘　　要：用径向基神经网络方法构造近似模型常常难以满足精度要求,提出了一种把二次响应面与径向基神经网络相结合的算法。该方法在样本点相同的情况下减小了近似模型的推广误差,提高了近似精度,增加了适应性。通过2个算例表明该算法提高了近似模型的精度,可在多学科设计优化中提高设计效率和质量。The paper presents an algorithm combining square response surface and Radial Basis Function Neural Network （RBFNN） to solve the problem that RBFNN is often difficult to meet the precision request of approximation model. By using this method the extended error of the approximation model is diminished, the approximation precision is improved, and the flexibility is enhanced based on having the same sample points. Two numerical examples indicate that the method is effective in increasing the approximation precision and can be used to increase the design efficiency and quality in multidisciplinary design optimization （MDO）.

关 键 词：近似模型 二次响应面 径向基神经网络 多学科设计优化 

分 类 号：V22[航空宇航科学与技术—飞行器设计]