RFID标签到达率的动态自适应灰色模型预测算法研究

Dynamic Self-adaptive Gray Prediction Algorithm for RFID Tag Arrival Rate

针对动态环境中RFID标签到达率估计问题,提出动态自适应灰色预测算法来预测标签到达率。该算法采用到达率反向突变规则知识判断到达率变化趋势,使建模长度可动态地自适应标签到达率变化,克服了预测准确性与跟踪速度之间的矛盾,由此提高了算法预测的准确性。在实验中,用正弦强度函数的非平稳泊松过程来模拟标签到达率的随机变化。结果表明:在少量数据的随机数据环境中,预测算法能够有效地预测数据,且准确性高于一般灰色预测算法和指数平滑算法。

To solve the RFID tag-arrival-rate prediction problem in dynamic environment, the tag-arrival-rate prediction algorithm,which can dynamically self adapt to the change of arrival rate, was proposed based on the gray model. The changing trend of arrival rate was judged by the rule knowledge denoting reverse mutation of the tag arrival rate, in or- der that the model length can dynamically self adapt to the arrival rate change, as a result, the contradiction between prediction tracking speed and accuracy was overcome, which improves the prediction accuracy. The tag-arrival-rate change was modeled by the Non-Homogeneous Poisson Processes with sine intensity function. Simulation experiments show that the algorithm can effectively predict the arrival rate based on a few data in a few random data environment, and the accuracy of prediction is above general gray prediction algorithms and exponential smoothing algorithm.