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.
针对直觉模糊粗糙逻辑（IFRL）推理的规则库检验问题，提出了IFRL规则库的互作用性检验方法。
To the rule-bases checking issue with intuitionistic fuzzy rough logical(IFRL) reasoning, an interactivities checking approach to IFRL rule-bases is proposed.
在系统实现中，采用隶属函数来反映故障对象特征的模糊性和模糊关系，基于关系数据库进行知识的模糊表达，实现基于规则的模糊推理。
In the implement of system, we use membership function to mirror faults fuzzy and fuzzy concern, relational database to describe knowledge and rules, and perform ruled-based inference.
当在知识库中搜索到相应的知识规则后，采用T - s模糊推理模型的求解策略进行求解。
When the knowledge rule is found in the knowledge files, T-S fuzzy inference model is applied to resolution of question.
当在知识库中搜索到相应的知识规则后，采用T - s模糊推理模型的求解策略进行求解。
When the knowledge rule is found in the knowledge files, T-S fuzzy inference model is applied to resolution of question.
应用推荐