摘要
该文利用具有良好小样本学习能力的支持向量机回归拟合结构响应的显式函数,计算随机变量的灵敏度系数,并结合蒙特卡洛法对结构响应的随机性进行分析。采用自适应混合粒子群法优化支持向量机相关参数取值,提高了计算效率。通过两个工程算例验证了该方法的可行性,并对比了训练样本抽样方法对计算精度的影响。算例结果表明:利用补充抽样方法抽取训练样本计算结构随机性得到的结果精度高,拟合的概率密度分布曲线可以更好的反映真实情况;同时利用灵敏度系数研究了算例中不同随机变量对结构响应的敏感性。
The SVM （support vector machine） which possesses significant learning capacity at a small amount of information and generalization is used to regress the explicit function of structural responses. Based on the explicit function, the sensitivity coefficients of random variables are calculated. And combined with the Monte Carlo simulation, the stochastic analysis of structures can be done. The adaptive hybrid particle swarm is applied to optimize the parameters of the SVM so as to improve the computational efficiency. In order to verify the feasibility of this method, two engineering examples are analyzed so as to contrast the effect of the training samples method on the calculation accuracy. The results from these examples indicate that the complementary sampling method can achieve a more precise stochastic result in the extraction of the training samples, and the fitting probability density curves can better reflect the true situation. Meanwhile, the structure response sensitivity of the two examples is studied by the sensitivity coefficients of random variables.
出处
《工程力学》
EI
CSCD
北大核心
2014年第2期195-202,共8页
Engineering Mechanics
基金
国家自然科学基金项目(51208431)
高等学校博士学科点专项科研基金课题项目(20090184120033)
中央高校基本科研业务费专项资金项目(SWJTU12CX064)
西南交通大学创新基金项目
关键词
支持向量机
蒙特卡洛法
自适应混合粒子群法
随机性
灵敏度系数
support sector machine
Monte Carlo simulation
adaptive hybrid particle swarm optimization
stochastic
sensitivity coefficient
