| 基于模糊遗传算法的神经模糊控制器的综合优化 |
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| 引用本文： | 戚志东,朱新坚,朱伟兴. 基于模糊遗传算法的神经模糊控制器的综合优化[J]. 计算机仿真, 2004, 21(6): 122-126 |
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| 作者姓名： | 戚志东 朱新坚 朱伟兴 |
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| 作者单位： | 上海交通大学自动化系燃料电池研究所,上海,200030;江苏大学电气信息工程学院,江苏,镇江,212013 |
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| 基金项目： | 国家 8 63项目 (编号 :2 0 0 2AA5 170 2 0 ) |
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| 摘 要： | 针对遗传算法的特点，提出一种用模糊控制的方法来调整交叉概率和变异概率的改进模糊遗传算法及其算法结构，并将其应用于神经模糊控制器的综合优化设计。在以二阶模型为控制对象的仿真结果表明，此种模糊遗传算法不仅加快了解的收敛速度，而且大大提高了解的质量。
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| 关 键 词： | 模糊遗传算法 神经模糊控制器 交叉概率 变异概率 |
| 文章编号： | 1006-9348(2004)06-0122-05 |
| 修稿时间： | 2003-10-27 |
Integrated Optimization of Neuro-fuzzy Controller Based on Fuzzy Genetic Algorithm |
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| QI Zhi-dong,ZHU Xin-jian,ZHU Wei-xing. Integrated Optimization of Neuro-fuzzy Controller Based on Fuzzy Genetic Algorithm[J]. Computer Simulation, 2004, 21(6): 122-126 |
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| Authors: | QI Zhi-dong ZHU Xin-jian ZHU Wei-xing |
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| Affiliation: | QI Zhi-dong~1,ZHU Xin-jian~1,ZHU Wei-xing~2 |
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| Abstract: | Based on the characteristics of genetic algorithm, an improved fuzzy genetic algorithm (FGA) and its algorithm structure are proposed, in which the crossover probability and mutation probability are adjusted by fuzzy control method. Then FGA is used into the integrated optimal design of neuro-fuzzy controller. The results of simulation on the second order control model show that this kind of FGA can improve both the convergent speed and the quality of the solution. |
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| Keywords: | Fuzzy genetic algorithm Neuro-fuzzy control Crossover probability Mutation probability |
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