一种小样本民机产品的可靠性预测方法  

Reliability Prediction Algorithm for Civil Aircraft Products Based on Small Sample

作　　者：王烨[1] 左洪福[1] 蔡景[1] 吕德峰[1] 

机构地区：[1]南京航空航天大学民航学院,南京210016

出　　处：《南京航空航天大学学报》2014年第1期170-174,共5页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：国家自然科学基金(61079003;61079013)资助项目;国家高技术研究发展计划("八六三"计划)(2006AA04Z427)资助项目

摘　　要：将最小二乘支持向量机(Least square support vector machine,LS-SVM)应用于小样本民机产品的可靠性预测分析。通过重构相空间的饱和嵌入维数,确定最小二乘支持向量机的最佳输入变量;然后,使用最小二乘向量机建立可靠度回归预测模型,运用自动网格搜索法,优化了最小二乘支持向量机的建模参数,实现了比现有方法精度高、泛化性好的模型。训练和测试的可靠性样本取自某机型襟翼液压锁寿命可靠性数据。与神经网络模型的比较实例表明,提出的方法合理有效。The least square support vector machine （LS-SVM） is used to predict the reliability of civil air craft products based on small sample. The optimized input variable number of LS-SVM is determined through computing the saturated embedding dimension of reconstruct phase space. Then, a reliability prediction model is established by using LS-SVM and their parameters are also optimized by an automat ic grid search method. The training and validation use reliability data from the hydraulic lock of a certain type aircraft. Finally, the one step and N-step prediction results of LS-SVM and radical basis function （RBF） neural network are compared, and show that the algorithm is feasible and valid for reliability prediction based on small sample.

关 键 词：小样本 民机产品 可靠度预测 支持向量机 参数优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]