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中文摘要：
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。
英文摘要：
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）.
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