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.
算法用贝耶斯推理处理不确定信息，量化地评估系统安全状态，并且有效地消除误报。
The algorithm handles uncertain information with Bayesian inference, giving a quantitative evaluation of the security state of a system and eliminating false alarms effectively.
计算力学是一个全新的理论框架，主要研究动力学系统中的几何状态空间如何支持符号推理计算。
Computational mechanics is a new theory frame to analyze how geometric state space structures support computation in dynamics systems.
因而涂层保护寿命预测系统的建立，需要在包含大量不确定信息的灰色状态下进行推理。
To build life prediction system of coatings requires using gray system which suit for dealing with the case that contains a great number of uncertain information.
基于专家系统工具，开发了TBM状态监测和故障诊断专家系统，阐述了系统的结构原理、推理规则、目标实现过程。
Situation monitoring and trouble diagnosis expert system for TBM is developed. The structure principle, inference rule and goal attaining process are described.
状态评估是建立在操纵员与系统及环境之间紧密交互基础之上的动态认知过程，其核心认知成分是推理。
Situation assessment is a dynamic cognitive process which is built on the close interaction between operators and systems. Its dominant component is reasoning.
状态评估是建立在操纵员与系统及环境之间紧密交互基础之上的动态认知过程，其核心认知成分是推理。
Situation assessment is a dynamic cognitive process which is built on the close interaction between operators and systems. Its dominant component is reasoning.
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