Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (3): 105-106.

• 网络、通信、安全 • Previous Articles     Next Articles

Algorithm of microcanonical—SVM based for forecasting traffic load

YAO Shihong1, WANG Tao1, JIA Zhenhong1, QIN Xizhong1, CHANG Chun2, WANG Hao2   

  1. 1.Institute of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
    2.Xinjiang Mobile Communication Company, Urumqi 830063, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2012-01-21 Published:2012-01-21


姚世红1,王 涛1,贾振红1,覃锡忠1,常 春2,王 浩2   

  1. 1.新疆大学 信息科学与工程学院,乌鲁木齐 830046
    2.中国移动 新疆分公司,乌鲁木齐 830063

Abstract: Basing on the speciality of traffic load in the paper, a traffic load forecasting model based on microcanonical annealing—Support Vector Machines(SVM) is proposed. Appropriate parameters are very crucial to SVM forecasting ability, the optimal parameters selection is achieved by random walks of demons in the state space of Microcanonical Annealing(MA) algorithm. The verification on the model with real traffic data shows that, this algorithm will offer better results with higher probability to hit the global optimum than Simulated Annealing(SA) algorithm, objective function value is also decreased faster, and has high precision.

Key words: traffic load, microcanonical annealing, Support Vector Machine(SVM), forecasting model

摘要: 根据话务量数据的特征,首次提出了一种基于微正则退火算法和支持向量机的预测模型。支持向量机参数的选择影响其预测的能力,微正则退火算法而是通过在状态空间中随机行走的虚拟妖来实现参数的优化选择。实际的话务量数据验证表明,其搜索成功率远高于模拟退火算法,目标函数值下降更快,能在短时间内搜索到最优解,且预测精度高。

关键词: 话务量, 微正则退火, 支持向量机, 预测模型