A design scheme of optimal RBF fuzzy neural network controller is proposed based on artificial immune principle.
提出了一种基于人工免疫原理的最优r BF模糊神经网络控制器设计方案。
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。
In the control system, Bayes probability is introduced in the fuzzy RBF neural network and it intensity the inference ability and increase the servo precision.
在控制系统中，将贝叶斯概率引入到模糊rbf神经网络中，增强了系统的推理能力，提高了飞机各个航道位置的模拟伺服精度。
The model forecasts the daily load by the nonlinear approaching capacity of the RBF neural network, than corrects the errors by on-line self-tuning factors of fuzzy control.
该模型利用RBF神经网络的非线性逼近能力对预测日负荷进行了预测，并采用在线自调整因子的模糊控制对预测误差进行在线智能修正。
The model forecasts the daily load by the nonlinear approaching capacity of the RBF neural network, than corrects the errors by on-line self-tuning factors of fuzzy control.
该模型利用RBF神经网络的非线性逼近能力对预测日负荷进行了预测，并采用在线自调整因子的模糊控制对预测误差进行在线智能修正。
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