The advantages of this adaptive fuzzy control method compared to other nonlinear control method (artificial neural network control) are analyzed.
分析了该方法与其它非线性控制方法(神经网络控制)相比所具有的优点。
Besides, an adaptive neural fuzzy control method is proposed to control the system, simulation results show the control method can better improve the steering portability and sensitiveness.
本文对电动助力转向系统设计了自适应模糊神经网络控制器，仿真结果表明该控制器能较好提高汽车转向时的轻便性和灵敏性。
This paper proposes a new method to learn fuzzy control rules using adaptive neural element.
本文给出了利用自适应神经元学习、修改模糊控制规则的新方法。
On the basis of this, a fuzzy-neural forecast controller is designed and robust adaptive control to the nonlinear big-lagged chaos system is realized.
在此基础上，又设计了模糊神经网络预测控制器，实现了对非线性、大时滞系统高精度的自适应控制。
A dynamic neural fuzzy model (DNFM) based adaptive control algorithm for ship course control was developed to overcome uncertainties arising from changes of model parameters.
针对船舶航向控制中模型参数变化引起的不确定性问题，提出一种基于动态神经模糊模型(DNFM)的自适应控制算法。
Then, applying adaptive control system based on improved fuzzy neural network, a TCBR controller is designed.
然后应用一种改进的模糊神经网络自适应控制系统，设计了TCBR的控制器。
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.
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。
Aiming at chaotic system, this paper proposes a multi-model adaptive control strategy based on a neural-gas network with fuzzy logic.
针对混沌系统的控制问题，提出了一种基于神经气网络的模糊多模型自适应控制方法。
The neural network-based adaptive control and fuzzy logic are integrated based on feedback learning algorithm.
在反馈学习算法的基础上，将模糊逻辑和神经网络自适应控制的结构结合在一起。
An online adaptive fuzzy neural network identification and robust control approach were proposed for the adaptive control problem of SISO nonlinear system.
针对单输入单输出非线性系统的自适应控制问题，提出了一种在线自适应模糊神经网络辨识与鲁棒控制的方法。
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。
This thesis propose an adaptive fuzzy control method based on BP neural network decoupling for typical thermodynamic processes and study the control system.
本文将针对这一热工过程，提出基于BP神经网络解耦的自适应模糊控制方法。
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。
Based on adaptive backstepping control techniques and fuzzy-neural theory, a sliding mode control scheme is proposed for missile control systems with uncertainties.
阐述了在导弹系统存在不确定性情况下，基于自适应反演控制技术和模糊神经网络理论，提出了一种导弹滑模控制系统设计方法。
By means of an identified adaptive neural fuzzy inference system (ANFIS) model of the excess air factor, the simulation of static state air fuel ratio feed-forward control was carried out.
借助于辨识的过量空气系数自适应神经网络模糊推理系统(ANFIS)模型，进行了静态空燃比前馈控制仿真。
By means of an identified adaptive neural fuzzy inference system (ANFIS) model of the excess air factor, the simulation of static state air fuel ratio feed-forward control was carried out.
借助于辨识的过量空气系数自适应神经网络模糊推理系统(ANFIS)模型，进行了静态空燃比前馈控制仿真。
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