论文部分内容阅读
基于GM(1,1)模型的模拟或预测结果具有严格单调性,导致其难以实现对随机波动序列的有效模拟,而以累加序列模拟值作为累减还原参数的建模方式是导致GM(1,1)模型精度不理想的主要原因。为了提高GM(1,1)模型模拟及预测精度,在传统灰色预测模型建模基础上,提出了基于改进累减还原方法的新GM(1,1)模型,然后应用该模型对城市短时交通流进行了模拟和预测,并将结果与传统GM(1,1)模型进行了比较和分析,结果显示新模型具有更加良好的模拟及预测性能。
The simulation or prediction results based on the GM (1,1) model are strictly monotonic, which makes it difficult to effectively simulate the random fluctuation sequence. The modeling method based on the accumulated sequence simulation value as the reduction and reduction parameter leads to the GM 1) The main reason for poor model accuracy. In order to improve the simulation and prediction accuracy of GM (1,1) model, a new GM (1,1) model based on improved reduction and reduction method is proposed based on the traditional gray forecasting model. Then, Traffic flow is simulated and predicted. The results are compared with the traditional GM (1,1) model. The results show that the new model has better simulation and predictive performance.