Self-organizing fuzzy inference system by Q-learning

Min-Soeng Kim, Sun-Gi Hong, Jujang Lee
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引用次数: 10

Abstract

The fuzzy inference system (FIS) is an expert system based on if-then rules which are extracted from experts' knowledge. To obtain experts' knowledge, however, is not always easy and may be expensive. Q-learning is one type of reinforcement learning in which the desired sequence of actions can be obtained by trial and error without a priori knowledge about the model. In this paper, the extended rule and the interpolation technique are proposed to combine FIS and Q-learning. The resulting self-organizing fuzzy inference system by Q-learning (SOFIS-Q) has the capability of generating the fuzzy rule base automatically and on-line by trial and error without any experts' knowledge.
基于q学习的自组织模糊推理系统
模糊推理系统(FIS)是一种基于if-then规则的专家系统,它是从专家的知识中提取出来的。然而,获得专家的知识并不总是那么容易,而且可能很昂贵。q学习是一种强化学习,在这种学习中,期望的动作序列可以通过试错获得,而不需要先验的模型知识。本文提出了FIS和Q-learning相结合的扩展规则和插值技术。基于q学习的自组织模糊推理系统(SOFIS-Q)具有在不需要任何专家知识的情况下,通过试错自动在线生成模糊规则库的能力。
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