基于优化策略的不确定数据流预测方法  

Forecast Method for Uncertain Data Stream Based on Optimal Policy

作　　者：徐雪松[1] 李玲娟[1] 郭立玮[2] 

机构地区：[1]南京中医药大学信息技术学院,南京210046 [2]南京中医药大学中医药研究院,南京210046

出　　处：《计算机工程》2011年第21期17-19,22,共4页Computer Engineering

基　　金：国家自然科学基金资助项目(30873449);南京中医药大学校青年自然科学基金资助项目(09XZR27)

摘　　要：为解决不确定数据流的预测问题,根据数据流高速、无限和动态不确定性的特点,在复杂人工智能预测和时间序列预测的基础上,提出一种基于优化策略的预测方法。综合考虑数据流中元组的不确定性与不确定异常性,以降低预测计算代价。同时考虑不确定的统计特性对卡尔曼滤波预测的影响,对Q和R进行异步优化估计,以形成最佳状态预测。实验结果表明,该方法的预测性能较好。On account of data stream for high speed,unlimited and dynamic characteristics of uncertainty,sophisticated artificial intelligence forecasting methods and the rapidness of times series forecasting method is used.A forecast method foruncertain data streams based on optimal policy that combines data stream tuples uncertainty and uncertainty abnormality for reducing the computational cost of forecast is proposed.Taking into account the statistical properties of the Kalman filteing prediction uncertainty on the impact of Q and R,Q and R are estimated by the innovation based asynchronous adaptive estimated at the same time.Experimental results on actual data source show that this method can adapt to the uncertain of data streams well and provide precise instantaneous detection under certain conditions.

关 键 词：时间序列 不确定数据流 优化估计 卡尔曼滤波 复杂度 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]