A new method of neural fuzzy predictive control using ordered neural networks and a improved fuzzy controller is presented.
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。
The predictive error and its rate of change of the gray predictive model are taken to the fuzzy controller as two input variables.
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。
Simulation results show the superiority of the proposed method over the conventional fuzzy controller and the grey model predictive fuzzy controller with fix predictive step size.
通过仿真研究验证了该方法能明显地改善常规模糊控制和固定预测步长的灰色预测模糊控制效果。
Fuzzy controller based on T-S model and generalized predictive controller are adopt.
控制采用基于T - S模型的模糊控制器和广义预测控制器。
A predictive control method of fuzzy internal model is proposed. It adopts internal model control, fuzzy predictor and fuzzy controller which are combined together to act on the controlled process.
采用内部模型控制、模糊预估器和模糊控制器相结合，对模糊内模预估控制方法进行了研究。
The Fuzzy-predictive control system uses the Fuzzy-PI control algorithm of parameter self-adjusting and the design process of off-line key part of fuzzy controller in this system is given in detail.
该模糊预测控制系统采用了带参数自整定的模糊-积分复合控制算法，详细给出了离线部分的设计过程，并提出了可用于工程实现的参数自整定算法。
The Fuzzy-predictive control system uses the Fuzzy-PI control algorithm of parameter self-adjusting and the design process of off-line key part of fuzzy controller in this system is given in detail.
该模糊预测控制系统采用了带参数自整定的模糊-积分复合控制算法，详细给出了离线部分的设计过程，并提出了可用于工程实现的参数自整定算法。
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