本文使用有序神经网络和改进的模糊控制器构成了一种新型的神经模糊预测控制方法。
A new method of neural fuzzy predictive control using ordered neural networks and a improved fuzzy controller is presented.
使用预测输出，在线辨识电机参数，通过模糊控制器完成对参考信号的跟踪。
Through predictive output and identification of motor's parameters on line, this fuzzy control method ensured the output to track the reference signals.
这是因为在相关实验中的控制器算法设计在基本原理上具有一致性。所应用的控制算法包括PID、模糊、DMC预测控制以及对象过程特性的继电反馈辨识。
In succession, the paper introduces the control algorithms used in experiment, which include PID control, fuzzy logic control, DMC predictive control and relay feedback identification.
如果判断出系统存在混沌现象，则设计模糊神经网络预测控制器，实现了对电力系统混沌振荡的预测控制。
If there is chaos oscillation in the power systems, a fuzzy-neural forecast controller is designed and forecast control is realized to the chaos oscillation.
在此基础上，又设计了模糊神经网络预测控制器，实现了对非线性、大时滞系统高精度的自适应控制。
On the basis of this, a fuzzy-neural forecast controller is designed and robust adaptive control to the nonlinear big-lagged chaos system is realized.
模糊控制器的两个输入变量为设定值，与灰色预测模型的输出值之间比较得到预测误差及其预测误差的变化率。
The predictive error and its rate of change of the gray predictive model are taken to the fuzzy controller as two input variables.
控制采用基于T - S模型的模糊控制器和广义预测控制器。
Fuzzy controller based on T-S model and generalized predictive controller are adopt.
该算法将预测控制与多模型思想结合，通过模糊自适应加权算法计算权重，采用动态矩阵控制优化控制器参数。
The weights are calculated by fuzzy adaptive weight algorithm and the controllers are optimized by dynamic matrix control algorithm.
由于T_S模糊模型每条规则的结论部分是一个线性模型，因此整个模糊模型可以看作一个线性时变系统，从而将模糊预测控制器中的非线性优化问题转化为一个线性二次寻优问题，以方便求解。
Since the conclusion part is linear, the T_S fuzzy model can be treated as a linear time_varying system, the nonlinear program in NMPC turns into a linear quadratic problem that can be easily solved.
文中依据非线性舰船模型，应用模糊神经网络简化出适应于舵减横摇控制器设计的模糊线性模型，并设计了广义预测控制器。
According to the nonlinear model of ship, a predigested fuzzy linear model is built adapted to rudder roll stabilization using fuzzy neural networks.
文中依据非线性舰船模型，应用模糊神经网络简化出适应于舵减横摇控制器设计的模糊线性模型，并设计了广义预测控制器。
According to the nonlinear model of ship, a predigested fuzzy linear model is built adapted to rudder roll stabilization using fuzzy neural networks.
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