改进的K近邻非参数回归在短时交通流量预测中的应用

针对现有K近邻非参数回归方法的局限,为了进一步提高算法的精度和速度,做出了两方面的改进:利用相关性理论选择状态向量和采用基于聚类分析的变K近邻搜索算法.用仿真实验验证了该方法的有效性,给出了仿真试验结果.实例研究结果表明,预测效果优于传统的非参数回归方法.

Improved K nearest neighbor nonparametric regression and its application in short-term traffic flow forecasting

To address the shortage of the K-nearest Neighbor Nonparametric Regression,and to improve the accuracy and computing speed of the proposed algorithm,a method for improving the accuracy and computing speed is presented: choosing state vector based on self-association or association analysis and using an improved variable K searching method based on clustering analysis,simulation experiments are conducted to examine the validity of the method.The calculated results show that this method can produce more exact forecasting performance than the traditional one.