We study expert PID control, fuzzy adaptive PID control, RBF neural network PID control, internal control based on RBF neural networks.
研究了专家PID控制、模糊自适应PID控制、基于RBF神经网络整定的PID控制、基于RBF神经网络的内模控制。
Due to the temperature object is delay, the control result is not very satisfactory for the control results of traditional PID, fuzzy control, or neural network control.
由于该温度对象具有滞后特性，采用传统的PID控制、模糊控制或神经网络控制等单一的控制策略效果并不十分理想。
According to the requirement of the GICA strip quantitative detection system, the fuzzy neural networks PID is used by combining fuzzy logic and artificial neural network.
针对金免疫层析定量测试的要求，将模糊逻辑与人工神经网络相结合，采用模糊神经网络隐式PID控制作为控温方法。
Simulation experiment compared with the double-loop motor subject to adaptive PID control, fuzzy control, neural-network control and conventional PID control is presented.
并与国内外研究较多的无刷直流电机的基于自适应PID、模糊控制、神经网络控制、PID控制的双闭环控制系统进行仿真对比实验。
Simulation experiment compared with the double-loop motor subject to adaptive PID control, fuzzy control, neural-network control and conventional PID control is presented.
并与国内外研究较多的无刷直流电机的基于自适应PID、模糊控制、神经网络控制、PID控制的双闭环控制系统进行仿真对比实验。
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