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.
同时基于自适应神经模糊推理系统建立了岩体力学参数与边坡抗滑力和下滑力的映射模型，分析得到抗滑力和下滑力的统计特征。
The model is used to perform the numerical simulation of slope stable state, to acquire the data for adaptive neuro-fuzzy inference system(ANFIS) analysis.
由于自适应模糊神经网络系统具有非线性映射和自学习能力，能够用于噪声信号的非线性建模。
The AFNNS has the abilities of nonlinear mapping and self-learning property and can be used to achieve the nonlinear model of the noise.
由于自适应模糊神经网络系统具有非线性映射和自学习能力，能够用于噪声信号的非线性建模。
The AFNNS has the abilities of nonlinear mapping and self-learning property and can be used to achieve the nonlinear model of the noise.
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