Zhou Yi, Er Meng Joo
School of Electrical and Electronic Engineering, Singapore Polytechnic, Singapore 139651.
IEEE Trans Syst Man Cybern B Cybern. 2008 Aug;38(4):963-9. doi: 10.1109/TSMCB.2008.922053.
An evolutionary approach toward automatic generation of fuzzy inference systems (FISs), termed evolutionary dynamic self-generated fuzzy inference systems (EDSGFISs), is proposed in this paper. The structure and parameters of an FIS are generated through reinforcement learning, whereas an action set for training the consequents of the FIS is evolved via genetic algorithms (GAs). The proposed EDSGFIS algorithm can automatically create, delete, and adjust fuzzy rules according to the performance of the entire system, as well as evaluation of individual fuzzy rules. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFIS approach is superior to other related methods.
本文提出了一种用于自动生成模糊推理系统（FIS）的进化方法，称为进化动态自生成模糊推理系统（EDSGFIS）。FIS的结构和参数通过强化学习生成，而用于训练FIS后件的动作集则通过遗传算法（GA）进行进化。所提出的EDSGFIS算法可以根据整个系统的性能以及对单个模糊规则的评估，自动创建、删除和调整模糊规则。移动机器人在壁面跟踪任务上的仿真研究表明，所提出的EDSGFIS方法优于其他相关方法。
