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首页> 外文会议>Conference on Independent Component Analyses, Wavelets, and Neural Networks Apr 22-25, 2003 Orlando, Florida, USA >Training data requirement for a neural network to predict aerodynamic coefficients
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Training data requirement for a neural network to predict aerodynamic coefficients

机译：神经网络预测空气动力学系数的训练数据需求

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摘要

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.
机译：基本空气动力学系数被建模为迎角，速度制动器偏转角，马赫数和侧滑角的函数。大多数空气动力学参数可以使用多项式函数很好地拟合。先前我们证明了神经网络是预测空气动力学系数的一种快速，可靠的方法。在预测期间，我们几乎没有遇到过拟合和/或过拟合的结果。神经网络的训练数据来自风洞测试测量和数值模拟。出现的基本问题是：要产生有效的神经网络预测需要多少训练数据点，以及在输入隐藏层和隐藏输出层之间应使用哪种类型的传递函数。本文对基于不同传递函数和训练数据集大小的神经网络预测效率进行了比较研究。神经网络预测的结果反映了体系结构，传递函数和训练数据集大小的敏感性。

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