补偿模糊神经网络是综合补偿模糊逻辑和神经网络的混合系统。
Compensatory fuzzy neural network is a hybrid system which integrates compensation fuzzy logic and neural network.
该系统控制采用了补偿模糊神经网络算法和逐级模糊控制规则。
The Compensated Fuzzy-Neural network algorithm and the rules of the fuzzy control are applied to the system.
研究了基于补偿模糊逻辑与神经网络相结合的补偿模糊神经网络(CFNN)。
The compensative fuzzy neural network (CFNN) based on compensative fuzzy logic and neural network and its study arithmetic are researched.
本文介绍了一种具有快速学习算法、能够执行补偿模糊推理的补偿模糊神经网络。
The compensation fuzzy neural network (CFNN) with fast learning algorithm and compensation fuzzy inference is introduced in this paper.
通过补偿模糊推理和快速学习算法的引入，使得补偿模糊神经网络在性能上优于一般的模糊神经网络。
Through the introduction of compensatory fuzzy inference and quick arithmetic, the property of compensatory fuzzy neural networks is superior to that of common fuzzy neutral networks.
针对神经网络对水果进行分级时精度有待提高的问题，分析了补偿模糊神经网络椪柑形状分级器的分级误差。
This investigation analyses grading errors based on compensating fuzzy neural network to find out methods to improve citrus fruit grading.
通过对TE过程的故障诊断建模，结果表明该网络在建模精度和收敛速度上均优于常规补偿模糊神经网络和常规模糊神经网络。
Finally, the result of the fault diagnosis modeling of TEP shows that the proposed network is superior to the conventional FNN and CFNN in modeling precise and convergence rate.
在文中的第五章，对补偿模糊神经网络训练的规则用经过噪声污染的数据进行验证，结果表明，网络能比较真实的反应理论结果的变化趋势。
In the chapter 5 the trained fuzzy rule is confirmed by the data adding random noise, the result shows that compensatory fuzzy neural network can respond the trend of theory variety.
提出了一种带模糊补偿的神经网络辨识器，并应用在某型涡扇发动机转速控制系统中。
A new neural network algorithm with fuzzy logic compensation was proposed and applied to an aero-engine rotating speed control system.
采用RBF神经网络在线补偿不确定项和模糊建模误差，能够使飞机获得满意的控制效果。
Using RBF NN can restore the airplane to the normal state by online regulating the effect of the uncertainties and the error caused by fuzzy modeling.
采用RBF神经网络在线补偿不确定项和模糊建模误差，能够使飞机获得满意的控制效果。
Using RBF NN can restore the airplane to the normal state by online regulating the effect of the uncertainties and the error caused by fuzzy modeling.
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