【摘要】 为了减少预测模型的输入量,本文利用粗糙集理论智能数据分析的能力,对神经网络进行预处理,抽取关键成分作为神经网络的输入,从而确定粗糙神经网络的拓扑结构,设计了供热负荷预测的粗糙广义回归神经网络模型,并用实际数据进行了仿真检验。实验结果表明,该方法是有效的,而且对供热负荷预测具有较高的精度和可靠性。更多还原

【Abstract】 In order to reduce the input number of forecast model, a neural network modeling way based on rough set theory is proposed. The neural network is preprocessed by the intelligent data analysis capability of rough set theory, and the key components are extracted as the inputs of the neural network to determine original topology of the rough neural network. The heating load forecasting rough set general regression neural network model is designed, and the actual data are used for the simulation test. Experimental results show that the method is effective, and it gets high precision and reliability in the heating load forecasting.更多还原