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.
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.
应用推荐