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。
This paper proposes a design of the self adaptive learning fuzzy controller based on Genetic Algorithms optimization.
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.
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 artificial intelligence principle is introduced. Based on a fuzzy controller, a self-learning intelligence controller is designed.
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.
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。
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.
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