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- W2796317900 abstract "A simple and accurate surrogate model is extremely needed to reduce the analysis complexity of thermal characteristics for a stratospheric airship. In this paper, a surrogate model based on the Least Squares Support Vector Regression (LSSVR) is proposed. The Gravitational Search Algorithm (GSA) is used to optimize hyper parameters. A novel framework consisting of a preprocessing classifier and two regression models is designed to train the surrogate model. Various temperature datasets of the airship envelope and the internal gas are obtained by a three-dimensional transient model for thermal characteristics. Using these thermal datasets, two-factor and multi-factor surrogate models are trained and several comparison simulations are conducted. Results illustrate that the surrogate models based on LSSVR-GSA have good fitting and generalization abilities. The pre-treated classification strategy proposed in this paper plays a significant role in improving the accuracy of the surrogate model." @default.
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- W2796317900 date "2018-06-01" @default.
- W2796317900 modified "2023-10-14" @default.
- W2796317900 title "A surrogate model for thermal characteristics of stratospheric airship" @default.
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- W2796317900 doi "https://doi.org/10.1016/j.asr.2018.03.036" @default.
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