In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr...In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.展开更多
基金Project(51209167) supported by Youth Project of the National Natural Science Foundation of ChinaProject(2012JM8026) supported by Shaanxi Provincial Natural Science Foundation, China
文摘In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.
文摘体内血糖测试虽然可以准确测定黄酒的血糖生成指数(glycemic index,GI),但相关食品营养学研究在食品工业中的应用却因效率低、成本高、可重复性差以及涉及伦理道德问题而受限严重。该研究构建了黄酒的半动态体外消化模型,使用不同甜型的黄酒进行体外消化实验,绘制相应的消化曲线,对实验结果进行数学拟合,推导出一系列黄酒半动态体外消化参数,并测得不同甜型黄酒的人体体内真实GI。通过主成分分析、偏最小二乘回归(partial least squares regression,PLSR)和Lasso回归等多元统计方法,建立基于半动态体外消化参数的黄酒GI预测模型。结果表明,PLSR预测模型良好,误差率<10%,预测方程为GI=0.323X+64.898(R^(2)=0.964),其中X表示黄酒体外消化胃阶段第一次胃排空时葡萄糖变化曲线下面积和单次胃排空时间的比值。该研究所提出的半动态体外消化模型和GI预测方程为黄酒的饮后血糖响应提供了一种快速、准确的分析方法。
