基于灰色RBF神经网络模型的电梯层间交通分布预测  

Forecasting of Elevator Interfloor Traffic Distribution Based on Grey RBF Neural Network

作　　者：杨祯山[1] 邵诚[1] 

机构地区：[1]大连理工大学电子与信息工程学院先进控制技术研究所,辽宁大连116024

出　　处：《信息与控制》2008年第6期690-696,共7页Information and Control

基　　金：国家973计划资助项目(2007CD714006);国家科技攻关计划资助项目(2001BA204B01);国家自然科学基金资助项目(69874026)

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.还提出了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.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.

关 键 词：电梯层澡交通分布 O-D矩阵 灰色预测 RSF神经网络 累加生成运算 累减还原 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP274[自动化与计算机技术—控制科学与工程]