基于BP神经网络的停车诱导泊位预测

研究了从历史停车数据中挖掘知识并预测短时段内停车泊位数问题.分析了停车诱导系统中影响停车泊位数的因素,结合时间序列确定网络的输入变量,建立BP神经网络,在不同训练阶段采用自适应调整学习速率,以及增加动量项改善网络的收敛性,运用Matlab对采集的市区大型地下停车场真实数据进行仿真实验与分析,取得良好预测效果.结果表明该方法与传统时间序列预测方法相比,在预测的精度方面有较大程度提高.

Prediction of Parking Guidance Space Based on BP Neural Networks

The problem of excavating knowledge from historical parking data and forecasting the number of parking spaces in a short period is studied.By analyzing the factors that affect parking space, we establish a BP neural network in which the network input variables are defined through the combination of time series.Then, a self-adaptive studying rate is used in different stage of training and the momentum terms are added to improve the convergence of the network.According to the real data collected from a large underground parking in town, the simulation and analysis are executed based on Matlab, which results in well-accepted prediction effect.The conclusion shows that the proposed method can improve the prediction accuracy compared with the traditional time series prediction method.