论文提出一种模糊强化学习算法，通过模糊推理系统将连续的状态空间映射到连续的动作空间，然后通过学习得到一个完整的规则库。
In this paper, we propose a fuzzy reinforcement algorithm, which map continuous state Spaces to continuous action Spaces by fuzzy inference system and then learn a rule base.
首先，提出一种模糊Q学习算法，通过模糊推理系统将连续的状态空间映射到连续的动作空间，然后通过学习得到一个完整的规则库。
A fuzzy Q learning algorithm is proposed in this dissertation, which map continuous state Spaces to continuous action Spaces by fuzzy inference system and then learn a rule base.
该系统采用状态映照平面初始化方法、未知模式标定技术和在线识别技术，并结合知识库和规则推理的运用，有效地实现设备状态的分类。
The system adopts the techniques of initialization of state mapping plane, calibration of unknown pattern and on-line identification and this makes plant condition clustering efficiently.
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