【摘要】 矿业项目投资决策系统涉及到矿产品价格、生产成本、市场需求及风险利率水平等主要变量,其中,矿石价格起主导作用,且其波动性呈非线性特征,难以用经典的时间序列理论来预测,因而难以实现矿业投资决策系统的最优化。以铀矿资源为例,采用BP神经网络与自适应模糊推理系统(ANFIS)技术,并结合时间序列技术分别建立铀矿产品价格的BP神经网络和ANFIS时间序列模型,并对铀矿产品价格的预测进行了比较分析,研究结果表明,铀矿石价格的ANFIS时间序列比BP神经网络时间序列具有较好的预测效果。更多还原

【Abstract】 The policy decision system for mining project investment involves such main variables as mineral product price,production cost,market demand and risk interest level,of which ore price has the dominant role.It is in non-linear variation and difficult to forecast by using classic time series theory,leading to a difficult realization of the optimization of the policy decision system for mining project investment.Taking uranium resource as example,BP neural network and adaptive neuron-fuzzy inference system are used,in combination with time series technique,to build BPNN and ANFIS time series models of the uranium product respectively.Comparative analysis of the forecasts of the uranium product price is made.The result indicates that ANFIS time series can better forecast the price than BPNN time series does.更多还原