在分析不同的量化因子和比例因子对控制特性影响的基础上，提出了智能模糊控制方法。
A method of intelligent fuzzy control is presented based on the analysis of the control characteristic affected by different quantization factor and scaling factor.
在分析不同的量化因子和比例因子对控制特性影响的基础上，提出了智能模糊控制方法。
The effects of quantitative factor and proportionality factor on the performance of a system with fuzzy controller are discussed.
介绍了均匀设计的基本原理及其在模糊控制器的量化因子和比例因子优化设计中的应用方法。
The basic principle of uniform design and its application in the optimal design of the quantifying factors and the scale factor of a fuzzy controller are introduced.
通过优化模糊pi控制器的量化因子和比例因子，从而优化隶属函数，使控制系统具有很好的稳态和动态性能。
Though optimizing quantification factor and proportionality factor of Fuzzy-PI controller, optimizing the membership function, then make the system response has good dynamic and steady performance.
该方法利用参数调整控制器在线自动修正基本模糊控制器的量化因子和比例因子，从而改善基本模糊控制器对任意设定气压的控制性能。
The properties of the basic fuzzy controllers can be improved through compiling S-function that automatically corrects the quantizing factors Ke and Kc , as well as proportional factor Ku.
然后，在线部分采用超代遗传算法（HGGA）优化输入量化因子和输出比例因子，缩短算法运行时间，加快收敛速度。
In the on-line part, the quantization factors and the proportion factor are optimized by the hyper generation GA(HGGA)for reducing the computation time.
本文利用自调整模糊控制器对比例因子和量化因子进行了优化，取得了满意的效果。
The effects of quantitative factor and proportionality factor on the performance of a system with fuzzy controller are discussed.
本文利用自调整模糊控制器对比例因子和量化因子进行了优化，取得了满意的效果。
The effects of quantitative factor and proportionality factor on the performance of a system with fuzzy controller are discussed.
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