中文摘要：

基于 BP 神经网络和 Markov 链预报模型的新灰色被建议。为了联合预报的灰色，与神经网络当模特儿，灰微分方程等价于的一条重要定理时间反应模型，被分析预报模型(美国通用汽车公司(1,1 )) 的灰色的特征证明。基于这，当时，微分方程参数在网络被包括 BP 神经网络被构造，并且神经网络被从灰系统提取样品训练“知道的 s 数据。当 BP 网络被集成时，增白的灰微分方程参数被提取然后预报模型(GNNM (1,1 )) 的灰神经网络被造。以便在 GNNM (1,1 ) 减少随机的现象，在二个状态之间的州的转变概率被定义， Markov 转变矩阵由造在灰预报和实际价值之间的剩余序列被建立。因此，新灰预报模型(MNNGM (1,1 )) 被把 Markov 链与 GNNM (1,1 ) 相结合建议。基于上述讨论，三条不同途径为预报中国电要求被提出。由把美国通用汽车公司( 1,1 )和 GNNM ( 1,1 )与建议模型作比较，结果显示 MNNGM ( 1,1 )的绝对吝啬的错误是大约 0.4 次 GNNM ( 1,1 )和 0.2 次美国通用汽车公司( 1,1 )，并且 MNNGM ( 1,1 )的均方差是大约 0.25 次 GNNM ( 1,1 )和 0.1 次美国通用汽车公司( 1,1 )。

英文摘要：

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.