基于遗传算法获取模糊规则

Fuzzy rule extraction based on genetic algorithm

针对传统利用遗传算法(GA)直接获得的模糊规则所具有的局限性问题,提出了一种带有加权因子的模糊控制规则计算方法,并利用遗传算法对加权因子进行全局寻优,最终由最优加权因子计算生成模糊规则。该计算方法针对不同的模糊输入等级施加不同的加权因子,并能够利用加权因子的相关性与对称性完整地评估所有的模糊规则,减少无效规则对系统响应所造成的影响。性能对比实验表明,该模糊规则所构成的模糊控制系统在控制过程中超调量小,调节时间短,在模糊控制的应用中具有可行性;不同激励的仿真实验表明,该模糊规则所构成的模糊控制系统的控制效果不依赖于系统的激励信号,跟踪效果好,具有很强的鲁棒性。

To avoid the fimitations of the traditional fuzzy rule based on Genetic Algorithm （ CA）, a calculation method of fuzzy control rule which contains weight coefficient was presented. GA was used to find the best weight coefficient which calculate the fuzzy rules. In this method, different weight coefficients could be provided according to different input levels, the correlation and symmetry of the weight coefficients could be used to assess all the fuzzy rules and then reduce the influence of the invalid rules. The performance comparison experiments show that the system which consists of these fuzzy rules has small overshoot, short adjustment time, and practical applications in fuzzy control. The experiments of different stimulus signals show that the system which consists of these fuzzy rules doesn＇t rely on stimulus signal as well as having a good tracking effect and stronger robustness.