中文摘要：

为了有效预测电梯的层间交通分布状态，提出一种层间交通O—D矩阵的预测方法．该方法融合灰色预测和神经网络方法各自的优点，将灰色预测方法与RBF神经网络有机结合，构造灰色神经网络预测模型．利用灰色预测中的累加生成运算（accumulated generating operation，AGO）对原始观测数据进行变换，得到规律性较强的累加数据，作为神经网络的建模和训练样本．还提出厂对不良交通需求数据的修正方法，以进一步降低观测数据的随机性．所提方法既避免了灰色预测方法存在的理论误差，又提高了神经网络的训练速度和预测精度，适用于短期层间交通分布预测．仿真试验验证了该方法的有效性．

英文摘要：

In order to efficiently forecast the elevator interfloor traffic distribution state, a method to predict the interfloor traffic O-D matrix is presented. The method makes use of the advantages of both grey forecasting and neural network, and organically combines grey forecasting and radial basis function neural network to construct the grey method based radial basis function neural network （GM-RBFNN） forecasting model. The accumulated generating operation （AGO） in grey forcasting is used to converse the initial observed traffic data to obtain the accumulated traffic data with strong regularity which are employed to model and train the GM-RBFNN. Meanwhile, a technique which modifies the abnormal traffic data is presented to further reduce the randomness of the observed traffic data. The proposed method not only avoids the theoretical error of grey forecasting, but enhances greatly both the training speed and prediction accuracy of neural networks, so it is suitable for short period forecasting of elevator interfloor traffic distribution. Simulation experiments prove the validity of the proposed method.