Based on fuzzy neural network, this paper presents a self learning controller used to industrial kiln temperature system.
本文提出一种模糊神经网络自学习控制方法，并应用于窑炉温度控制系统中。
The simulation results prove that the neural network controller has self-learning and self-adaptive ability by comparison with PD controller. The position tracking control obtains satisfactory effect.
结果表明，相对于常规PD控制器，该神经网络控制器具有自学习、自适应功能，位置跟踪获得了满意的控制效果。
Both simulation and practical application results show that this controller has strong robustness and the capabilities of self learning and adaptive decoupling.
经仿真研究及实际运行表明，多层神经网络PID控制器具有很强的鲁棒性、自学习功能和自适应解耦功能。
This paper proposes a design of the self adaptive learning fuzzy controller based on Genetic Algorithms optimization.
本文提出一种基于基因算法优化的自学习模糊控制器的设计。
The results show that the neutral network controller has shelf-learning and self-adaptive functions, and has better control effect in speed tracking, compared with traditional PI controller.
仿真结果表明，相对于常规PI控制器，该神经网络控制器具有自学习，自适应功能，速度跟踪获得了满意的控制效果。
The neuron controller with its self-learning ability can realize the on-line learning algorithm, and it is adopted as the position controller of the AC servo system.
利用神经元的自学习功能实现神经元控制器的在线学习，并以神经元控制器作为交流伺服系统的位置调节器；
The part of neural network of the intelligent self-tuning PID controller based on BP network learns the learning sample.
由设计出的基于BP神经网络的智能自整定PID控制器的神经网络部分对学习样本进行学习。
A new type of adaptive PID controller using diagonal recurrent neural network (DRNN) is presented. An on-line learning algorithm based on PID parameter self-tuning method is given.
提出了一种基于对角回归神经网络的PID控制器结构，给出了PID参数在线自整定的学习控制算法。
The artificial intelligence principle is introduced. Based on a fuzzy controller, a self-learning intelligence controller is designed.
本文引用人工智能原理，在模糊控制器的基础上，设计出一种自学习智能控制器。
The PID controller based on BP neural networks is designed to realize control parameter self-learning and self-adjusting.
设计了基于BP神经网络的PID控制器，实现PID控制器参数自学习、自整定。
The result of simulation shows that this neural network fuzzy controller features self-learning and self-adaptive capabilities, and the purpose of on-line control is accomplished.
仿真结果表明，所设计的神经网络模糊控制器具有自学习、自适应等优点，达到了在线控制的目的。
This adaptive fuzzy controller is based on fuzzy inference rules self-learning without needing so much expert control rules, which solves the problem of acquiring MIMO fuzzy inference rules.
这种自适应模糊控制器基于模糊推理规则自学习和自调整的控制算法，无需知道太多的专家控制规则，因此解决了制冷系统MIMO模糊推理规则难以获取的问题。
This adaptive fuzzy controller is based on fuzzy inference rules self-learning without needing so much expert control rules, which solves the problem of acquiring MIMO fuzzy inference rules.
这种自适应模糊控制器基于模糊推理规则自学习和自调整的控制算法，无需知道太多的专家控制规则，因此解决了制冷系统MIMO模糊推理规则难以获取的问题。
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