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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Ensemble fixed-size LS-SVMs applied for the Mach number prediction in transonic wind tunnel
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Ensemble fixed-size LS-SVMs applied for the Mach number prediction in transonic wind tunnel

机译：集合固定大小的LS-SVM在跨音速风洞中的马赫数预测中的应用

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摘要

It is important to predict the Mach number in a transonic wind tunnel system. According to the aerodynamic mechanism, the Mach number is indirectly calculated from the total pressure (TP) in the stilling chamber and the static pressure (SP) in the test section. The high-dimensional input features and large-scale data are the main difficulties to build the TP and the SP models. The fixed-size LS-SVM is a popular method to build a nonlinear model for a large-scale problem. However, it is difficult to further improve the sparsity in the high-dimensional input space. Based on the multivariate fuzzy Taylor theorem, the feature subsets ensemble (FSE) method is proposed to deal with the high-dimensional problem. The set of direct, exhaustive, independent feature-space subdivisions forms the basis to develop FSEs. In the FSE, submodels are learned using substantially low-dimensional data sets and characterized by low complexity. The TP and the SP are estimated with the FSE-based ensemble fixed-size least squares support vector machines (LS-SVMs). Experiments show that the FSEs speed up both training and testing time that would otherwise be infeasible for individual, bagging, and random subspace (RS). FSE models meet the requirements of the forecasting speed, the accuracy and the generalization of the Mach number prediction.
机译：预测跨音速风洞系统中的马赫数很重要。根据空气动力学机理，马赫数是根据静压室内的总压力（TP）和测试部分的静压力（SP）间接计算得出的。高维输入特征和大规模数据是构建TP和SP模型的主要困难。固定大小的LS-SVM是一种流行的为大型问题建立非线性模型的方法。然而，难以进一步提高高维输入空间中的稀疏性。基于多元模糊泰勒定理，提出了特征子集集成（FSE）方法来解决高维问题。一系列直接，详尽，独立的特征空间细分构成了开发FSE的基础。在FSE中，使用实质上低维的数据集学习子模型，并且其子模型的复杂度较低。使用基于FSE的集合固定大小最小二乘支持向量机（LS-SVM）估算TP和SP。实验表明，FSE加快了训练和测试时间，而对于单独的，装袋的和随机的子空间（RS），这是不可行的。 FSE模型满足预测速度，准确性和马赫数预测泛化的要求。

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