基于一阶流体随机Petri网的回报建模  

Reward modeling based on first order fluid stochastic Petri nets

作　　者：卢光松[1] 葛运建[2] 

机构地区：[1]中国科学技术大学自动化系,安徽合肥230027 [2]中国科学院合肥智能机械研究所,安徽合肥230031

出　　处：《中国科学技术大学学报》2006年第8期828-833,共6页JUSTC

摘　　要：从回报机制与回报变量的定义、回报模型的建立等方面提出了基于一阶流体随机Petri网的回报模型的一般性建模方法.该方法既可用于建模马尔科夫随机系统,也可用于建模非马尔科夫随机系统.在一阶流体随机Petri网回报模型中,可建模率回报和冲量回报,并可允许累积回报影响被建模系统的动态行为以及回报率本身.The reward formalism and reward variables for first order fluid stochastic Petri nets （FSPNs） were defined. The general modeling approaches for reward models based on the first order FSPNs were described. The approaches could be used to model Markovian and non-Markovian stochastic systems. In the first order FSPN reward models, both rate rewards and impulse rewards can be modeled, and accumulated reward can be allowed to influence the dynamic behavior and reward rates of modeled systems.

关 键 词：回报模型 流体随机PETRI网 率回报 冲量回报 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]