Fuzzy model optimization based on cooperative evolutionary genetic algorithm

机译：基于协同进化遗传算法的模糊模型优化

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Interpretability and accuracy are base requirements of fuzzy model. An approach to construct fuzzy model based on co-evolutionary genetic algorithm is proposed for the requirements. First, the initial fuzzy system is identified using WM method because of its simplicity and quickness to generate fuzzy rules. The membership functions and fuzzy rule base are optimized by the co-evolutionary genetic algorithm in order to improve accuracy of the fuzzy model. The result of simulation experiment shows its validity.

机译：可解释性和准确性是模糊模型的基本要求。针对这种需求，提出了一种基于协同进化遗传算法的模糊模型构建方法。首先，使用WM方法识别初始模糊系统，因为它简单易行，可以生成模糊规则。通过协同进化遗传算法优化隶属度函数和模糊规则库，以提高模糊模型的准确性。仿真实验结果表明了其有效性。

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来源
《Electrical amp; Electronics Engineering (EEESYM), 2012 IEEE Symposium on》|2012年|p.599- 602|共4页
会议地点 Kuala Lumpur(MY)
作者
Mengli Qiao;
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正文语种 eng
中图分类电气化、电能应用;
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Fuzzy model optimization based on cooperative evolutionary genetic algorithm

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