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Risk Warning Model Based on Radial Basis Function Neural Network

机译：基于径向基函数神经网络的风险警告模型

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General contracted project is a very complicated system. The risk of general contractor gets greater and greater with the technology and free trade pressure after China enters WTO, so how to escape and disperse risk becomes a hot topic. Risk warning model is valuable to risk analysis because it can make general contractors have risk management capacity. A new method to analyze the risk of general contracted project is proposed, which uses the radial basis function(RBF)neural network to establish a risk warning model. The analytical hierarchy process(AHP)is carried out to determine the key factors, which are used as inputs of radial basis function neural network through numeralization. By training the neural network, we obtain the nonlinear function from key factors to risk compensation, and then the sensitivity of key factors is launched. The practical project sample analysis proves the validity of the method.
机译：一般收缩项目是一个非常复杂的系统。在中国进入WTO后，一般承包商的风险与技术和自由贸易压力变得更大，因此如何逃避和驱散风险成为一个热门话题。风险警告模型对风险分析有价值，因为它可以使总承包商具有风险管理能力。提出了一种分析一般收缩项目风险的新方法，它使用径向基函数（RBF）神经网络来建立风险警告模型。进行分析层次处理（AHP）以确定通过数字化用作径向基函数神经网络的输入。通过培训神经网络，我们从危险补偿的关键因素获得非线性功能，然后启动关键因素的敏感性。实际项目样本分析证明了该方法的有效性。

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