状态预测模型中的多步预测算法  

Multi-step Prediction Algorithm for State Prediction Model

作　　者：张自立[1] 李艳[2] 宋宏伟[1] 

机构地区：[1]石家庄学院计算机系,石家庄050035 [2]军械工程学院计算机工程系,石家庄050035

出　　处：《煤炭技术》2011年第12期170-171,共2页Coal Technology

摘　　要：一般系统的状态受多个因素的影响,而基于动态贝叶斯网的状态预测模型就能够较准确地描述系统状态和影响因素之间的关系。针对此模型,提出推理宽度的概念以减少推理过程中的数据量,并利用时间片扩充办法来对状态进行多步预测。The state of the system is generally affected by many random factors. The state prediction model based on dynamic bayesian network can more accurately describe the relationship between the system state and the influencing factors. According to this model, the span of the reasoning is proposed to reduce the amount of data in the reasoning process. Meanwhile the multi-step state prediction algorithm is achieved by extending time-slice.

关 键 词：动态Bayesian网 状态预测模型 预测 时间片 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]