Journal of Shandong University(Engineering Science) ›› 2013, Vol. 43 ›› Issue (1): 9-14.doi: 10.6040/j.issn.1672-3961.3.2012.047

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A modified general regression neural network with its application in traffic prediction

YI Liang-zhong1, ZHANG Chao2*, PEI Zheng3   

  1. 1. Department of Computer Science and Technology, Sichuan Police College, Luzhou 646000, China;
    2. Department of Road Traffic Management, Sichuan Police College, Luzhou 646000, China;
    3. School of Mathematic ＆ Computer Engineering, Xihua University, Chengdu 610039, China
  • Received:2012-12-05 Online:2013-02-20 Published:2012-12-05

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Abstract:

Based on the method of k nearest neighbors algorithm, the optimum smoothing parameter was found by means of network performance evaluation. The approach depended not only on the value of mean square error, but also could sort the mean square error without affecting the forcasting performance. The optimum smoothing parameter was difficult to be found because of the volatility of the data solved by the modified algorithm. Finally, a traffic forcasting experiment was provided to analyze the effectiveness of the proposed algorithm. The results revealed that the optimum smoothing parameter found by means of k nearest neighbors could obtain the minimum prediction error.

Key words: the general regression neural network, k nearest neighbors, the smoothing parameter

CLC Number: 

  • TP301
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