在此基础上提出了采用遗传算法对压实参数模糊控制规则进行优化的新思路。
We also propose novel ideas in optimizing the fuzzy control rules, using genetic algorithms.
同时，应用神经网络的学习和记忆功能，对控制变量的隶属函数和控制规则进行优化，使控制方案更趋于合理。
At the same time, using the study and memory ability of neural networks, optimize subject function and control rules of control variables, which makes the control scheme more reasonable.
用此方法，可以优化确定系统输入和输出变量的隶属函数以及模糊控制规则。
By this means, the membership functions of input and output variables and the fuzzy control laws can be optimized.
本文针对单瓶颈节点网络，考虑两个饱和非线性因素，制定控制规则，寻找优化参数，设计模糊控制器。
In this paper, single bottleneck node network is considered. With two saturation factors, we make control rules, find optimal parameters and design the controller.
采用浮点数编码对模糊控制规则进行优化，既提高了运算效率和计算精度，又保证了控制系统的快速性和全局最优性。
Floating-point coding is adopted to optimize fuzzy control rules, it can improve the efficiency and accuracy of calculation, also can guarantee fastness and global optimal.
针对模糊控制稳态精度较差的问题，提出了“双模”模糊控制器，通过调整因子来优化控制规则。
The dual mode ambiguity controller, which optimizes the controll rule by adjusting factors, works out the solution to the problem of poor stable precision of ambiguity controlling.
最后给出模糊控制规则模型自寻优优化方法。
The self-optimizing method of the fuzzy control rule model has been presented finally.
最后，论文提出采用遗传算法对模糊控制规则进行优化设计。
At last, the thesis suggests using the method of Genetic Algorithms to optimize the rules of fuzzy control.
提出了一种解析规则模糊控制器的改进结构，并引入了能够动态调整模糊控制规则的修正函数；同时，通过遗传算法，实现了模糊控制器控制参数的组合优化设计。
An improved structure of analytic expression based fuzzy controller is proposed, and a modifying function capable of regulating the fuzzy control rules dynamically is introduced.
提出了一种解析规则模糊控制器的改进结构，并引入了能够动态调整模糊控制规则的修正函数；同时，通过遗传算法，实现了模糊控制器控制参数的组合优化设计。
An improved structure of analytic expression based fuzzy controller is proposed, and a modifying function capable of regulating the fuzzy control rules dynamically is introduced.
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