• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >The modified feature subsets ensemble applied for the mach number prediction in wind tunnel
【24h】

The modified feature subsets ensemble applied for the mach number prediction in wind tunnel

机译：改进的特征子集集成在风洞马赫数预测中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Large-scale and high-dimensional data are the main difficulties for Mach number prediction in a wind tunnel system. To solve these issues, based on the multivariate fuzzy Taylor theorem, the Feature Subsets Ensemble (FSE) method has been proposed. However, data sampled from measurements have noise. The FSE uses the entire data set at least once, and for data with noise this property may reduce the generalization of Mach number prediction. In this paper, a Modified Feature Subsets Ensemble (MFSE) method is proposed to overcome the limitation of the FSE on noisy data by introducing the bootstrap to the feature subsets. The bootstrap has the potential to avoid noise. In addition, the generating procedure of the bootstrap replications is straightforward, simple, and quick, which keeps the complexity of the MFSE low. Experiments show that the MFSE outperforms the FSE and the Random Forest method, particularly on high-noise data sets. For all three working conditions, on both the low- and the high-noise testing sets, only the MFSE estimator meets the requirements of forecasting speed, accuracy, and generalization of Mach number prediction.
机译：大规模和高维数据是风洞系统中马赫数预测的主要困难。为了解决这些问题，基于多元模糊泰勒定理，提出了特征子集集成（FSE）方法。但是，从测量中采样的数据会产生噪声。 FSE至少使用一次整个数据集，并且对于有噪声的数据，此属性可能会降低Mach数预测的一般性。本文提出了一种改进的特征子集集成（MFSE）方法，通过将引导程序引入特征子集来克服FSE对噪声数据的限制。引导程序有可能避免噪音。此外，引导程序复制的生成过程非常简单，直接，快捷，这使MFSE的复杂性降低了。实验表明，MFSE优于FSE和随机森林法，特别是在高噪声数据集上。对于这三种工作条件，在低噪声和高噪声测试集上，只有MFSE估计器才能满足预测速度，准确性和马赫数预测的一般性的要求。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号