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。
Based on fuzzy neural network, this paper presents a self learning controller used to industrial kiln temperature system.
新控制器在控制过程中借助模糊神经网络的自学习算法实现控制参数的在线调整。
The parameters of new controller can be adjusted on line based on the ability of fuzzy ne ural network.
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。
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.
该系统依靠模糊控制理论提高了灵敏度，减少了误报率，并结合神经网络具有自学习功能的特点，提高了整个系统的智能化程度。
The system would reduce the malfunction rate with high sensitivity. Also the neural network has function of self-learning, which would raise the degree of intelligence of the system.
应用一种变结构神经网络算法对初始化的模糊规则进行调整，提高模糊控制规则的自学习和自适应能力。
A kind of variable structure neural network algorithm is adopted to adjust fuzzy rules, and improves the ability of self-studying and self-adjusting in fuzzy control rules.
将神经网络与模糊控制相结合，实现了模糊控制器的自学习和自适应。
Combine the neural network with controlling fuzzy control, the ones that have realized the fuzzy controller are from the self-study and self-adaptation.
将神经网络与模糊控制相结合，实现了模糊控制器的自学习和自适应。
Combine the neural network with controlling fuzzy control, the ones that have realized the fuzzy controller are from the self-study and self-adaptation.
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